450 results about "Comfort levels" patented technology

The invention provides an indoor environment monitoring method and system integrating temperature and humidity, and is applied to the field of sensing data processing. According to the method, the reading difference of the temperature sensor is collected, whether local deviation exists or not is judged, the arrangement type of the sensor is combined, a low temperature value generated by the wall-leaning sensor due to the cold bridge effect is dynamically compensated, misjudgment of an environment system is avoided, and the human body temperature is introduced as a sensing reference by constructing a temperature difference deviation function. According to the method, comprehensive temperature indexes closer to real experience can be generated, when abnormal fluctuation is detected, the space structure and heat source information are further combined, boundary conditions are constructed, dynamic heat flow and temperature and humidity distribution diagrams are simulated, temperature sensing deviation is effectively and reversely corrected, the precision, stability and energy-saving performance of an environment adjusting system are remarkably improved, and the comfort degree of a human body is improved.

The embodiment of the invention provides an energy-saving optimization system for heating, ventilating and air conditioning of an indoor ski field. The energy-saving optimization system comprises a multi-source sensing layer, an edge computing layer, a cloud decision-making layer and an equipment execution layer. The multi-source sensing layer is used for collecting multi-source data such as weather, passenger flow, temperature and humidity and equipment state; the edge calculation layer carries out fusion processing on the data and generates a load prediction result through a load prediction mechanism; the cloud decision-making layer generates an optimization control instruction based on a multi-agent deep reinforcement learning and model prediction control optimization strategy; and the equipment execution layer receives and executes the instruction and feeds back the equipment state. Through a multi-layer collaborative optimization architecture, accurate load prediction and equipment intelligent collaborative control are realized, five-stage stepped optimization and a dynamic priority mechanism are adopted, the energy efficiency of the system is remarkably improved, the energy consumption is reduced while the environmental comfort is ensured, and the economical efficiency and the stability of system operation are effectively improved.

The invention relates to the technical field of air-cooled air conditioner control, in particular to an air-cooled air conditioner control method and system based on deep learning, and the method comprises the following steps: obtaining sensor readings of indoor temperature, outdoor temperature and humidity and Wi-Fi channel state information, judging the position number and the activity state of personnel, calculating the position distribution of the personnel in combination with a time window, and calculating the position distribution of the personnel. And integrating to obtain a multi-dimensional environment and load state vector. According to the method, indoor and outdoor temperature, humidity, Wi-Fi channel state information and personnel position distribution and activity states are integrated through a multi-dimensional environment and load state vector, the personnel density change trend is dynamically calculated through a time window, the comprehensiveness and real-time performance of environment perception are enhanced, and high-dimensional data support is provided for control decision making. And a basic comfort degree deviation index is constructed based on the temperature deviation value and the humidity deviation value, and a multi-target performance index set is generated in combination with the wind speed value, the compressor start-stop frequency and the energy consumption power.

The embodiment of the invention discloses a self-adaptive air conditioner heating control method and system based on multi-source perception.The method comprises the steps that multi-source environment data such as indoor environment data, outdoor environment data and air conditioner operation data are collected in real time, and active adjustment behaviors and environment state data of a user are combined; a personalized comfort model is established and continuously updated through a machine learning algorithm, and a dynamic target comfort temperature interval is output to reflect comfort temperature preferences of a user in different environments; the multi-source environment data and the target comfortable temperature interval serve as input, maximization of user comfort, minimization of energy consumption and reduction of compressor starting and stopping times serve as targets, rolling optimization calculation is conducted through an optimization algorithm, and an air conditioner control instruction set containing a dynamic target temperature set value, a compressor operation instruction and a fan rotating speed instruction is dynamically generated; and executing an instruction set, monitoring an effect and user feedback, and feeding data back to the model establishment and optimization calculation step to realize self-adaptive control.

The invention relates to the technical field of smart home, and discloses an electric blanket system capable of adjusting calorific value by self-adaption to environment temperature, comprising a sleep sensing module used for collecting physiological signal data of a user; the state recognition module is used for recognizing the current sleep state of the user; the temperature target generation module is used for determining a corresponding target temperature value according to the current sleep state; the control calculation module is used for calculating a control instruction of each heating area; the multi-area heating execution module is used for performing heating control on the plurality of independent heating areas; and the temperature acquisition and feedback module is used for acquiring the real-time temperature of each heating area and feeding back the temperature to the control calculation module. Through linkage of the AI housekeeper and the external electronic bracelet, adaptive adjustment of the intelligent temperature control system is realized, a temperature control strategy is dynamically optimized according to real-time temperature data, user behaviors and environmental changes, limitation of a traditional static scheme is avoided, personalized temperature adjustment is provided, and flexibility and comfort are improved.

The invention discloses a temperature control method and system based on a time sequence-residual module cascade algorithm, and belongs to the field of heating and ventilation design and automatic control design. The method comprises the following steps: acquiring environmental data in real time by using a sensor system, and generating a data set suitable for model training and prediction through data preprocessing and feature extraction; a deep learning model is awarded, wherein a time sequence-residual module cascade model is adopted for temperature prediction; the network structure captures the long-term trend of a time sequence by using the LSTM, the Res residual module extracts local fluctuation features by using a convolutional layer, and the local fluctuation features and the Res residual module realize cooperation of a time sequence dimension and a space dimension through feature parameter jump connection remodeling, so that the capability of predicting the future temperature change trend is remarkably improved, and the prediction efficiency is improved. The indoor temperature can be adjusted in real time to maintain comfortable environmental conditions. According to the intelligent indoor temperature prediction method, the comfort degree of the indoor environment is improved, the energy consumption is remarkably reduced, the remarkable energy-saving effect is achieved, and the positive promotion effect on environment protection is achieved.

The invention discloses an intelligent temperature control energy-saving system and method based on personnel position detection and prediction. The system monitors indoor personnel distribution and temperature and humidity in real time through the human body and environment sensing module; the behavior prediction module utilizes an LSTM deep learning model to analyze the historical moving track of the user and predict the next target room and the arrival time of the user in combination with the environment context; the environment regulation and control module starts equipment such as an air conditioner in the target room in advance to adjust to a comfortable state; and the energy efficiency optimization module is used for implementing energy-saving control on the unmanned room. According to the method, the crossing from passive reaction to active prediction is realized, and the energy utilization efficiency of the home environment is remarkably improved on the premise of ensuring the comfort.

The invention relates to the crossing field of biothermodynamics and dynamic environment control, and discloses a space thermal microclimate multi-target regulation and control method, device and system, a medium and a product. The method comprises the following steps: acquiring physiological parameters (including effective dressing thermal resistance and local sweat gland density of posture correction) of a passenger, environment space-time parameters and light environment parameters through a biological characteristic sensor, an environment sensor and an optical sensor; constructing a dynamic human body thermal comfort model library, and adopting a feature coding-parameter decoding mechanism to match a target thermal comfort model; generating a hierarchical regulation strategy based on the difference between the local comfortable temperature and humidity interval and the real-time thermal environment sensing parameter; and the refrigerant circulation system, the fluid circulation system and the optical radiation system are cooperatively controlled to realize local microclimate regulation and control. According to the method, the problems of inaccurate thermal comfort modeling and low multi-system cooperation efficiency in a transient environment are solved, the energy efficiency ratio is improved, and the comfort of passengers in a cabin is remarkably improved.

The invention provides a conference room environment regulation and control method and system, a medium and a product. In the method, an environment regulation and control system firstly determines a target temperature according to an external environment temperature, and then carries out temperature regulation and control in advance in combination with reservation time. When the conference is carried out, the environment regulation and control system carries out targeted monitoring on the temperature of different areas by dividing the air outlet area and the non-air outlet area in the conference room, and it is ensured that the temperature imbalance phenomenon is found in time. When the regional temperature difference between the air outlet region and the non-air outlet region reaches a temperature difference threshold value, the environment regulation and control system determines the regulation sequence according to the number of people in the region and the regional temperature difference, and it is ensured that resources are preferentially used for the region with the larger influence. And finally, the environment regulation and control system calculates the air volume and the air direction by combining the space parameters of the to-be-regulated area and regulates and controls the air volume and the air direction in sequence, so that the temperature of each area synergistically approaches a target value. According to the method, the temperature difference of different positions of the conference room is effectively relieved, and the balance and comfort of the whole environment are improved.

The invention discloses a self-adaptive adjusting method and device for a vehicle seat, equipment and a medium. The method comprises the steps that first temperature data detected by a temperature sensor in a vehicle and first humidity data detected by a humidity sensor in the vehicle are obtained; detecting first physiological feature data of a passenger of the target seat; inputting the first temperature data, the first humidity data and the first physiological feature data into a trained comfort level identification model, and predicting the comfort level category and the key index category of the passenger through the comfort level identification model; if it is determined that the comfort level category is uncomfortable, a to-be-adjusted assembly is determined from the heating assembly and the ventilation assembly according to the key index category; and controlling the to-be-adjusted assembly to adjust the temperature or humidity of the target seat. Adjustment of heating and ventilation of the vehicle seat can be automatically achieved, and the riding experience is improved. The method can be widely applied to the technical field of vehicles.

A device located within a user environment may be used to measure one or more environmental conditions within the user environment. For example, the sensor device may measure the temperature, humidity, light intensity, color temperature, and / or the like. The sensor device may periodically measure the environmental conditions, and may periodically transmit the values of the measured environmental conditions to a computing device. A mobile device associated with a user may receive the measured values from the sensor device when the mobile device is within range of the sensor device, and may present a survey to the user that includes one or more questions that prompt the user to report their comfort level with respect to the measured environmental conditions. The mobile device and / or another computing device may build a profile for the location and / or the user based on the values and the user's reported comfort levels.

The invention provides an intelligent closestool temperature adjusting method, a server, a medium and a program product, and relates to the technical field of intelligent control. The method comprises the steps that the initial skin temperature when a user makes contact with a seat ring and the indoor environment temperature are collected, a temperature change curve is established to determine the skin temperature sensing condition and the optimal temperature adjusting time, the two are combined to calculate the temperature sensitivity coefficient, and the target temperature is determined according to the temperature sensitivity coefficient and heated to the preset weight temperature within the optimal time. The skin temperature rising condition is detected to determine the optimal temperature, and heating is continued if the skin temperature does not rise. The corresponding relation between the optimal temperature and the use time period, the environment temperature and the sensitivity coefficient is stored in a database, when the same user is detected next time, the heating parameter is called according to the current environment temperature to adjust the temperature, and by implementing the method, the heating temperature is automatically adjusted according to the individual difference and the environment temperature, and the comfort level and the energy saving performance are improved.

The invention is suitable for the technical field of air conditioners, and provides an air conditioner intelligent regulation and control method which comprises the steps that outdoor environment data, indoor environment data and personnel state data are collected in real time, all the data are fused, and a comprehensive data set is generated; constructing a dynamic temperature gradient field based on the comprehensive data set; dividing functional areas, and calculating an influence relation matrix in combination with the dynamic temperature gradient field; taking the influence relation matrix as a core constraint condition, and adopting a multi-objective optimization algorithm to obtain an air conditioner regulation and control parameter set; and on the basis of the air conditioner regulation and control parameter set, the output parameters of all the air conditioner single bodies are gradually adjusted through a switching mechanism, dynamic global optimization of the air conditioner system on multiple targets such as energy saving, comfort and equipment service life is achieved, and the problems that regulation and control are lagged, energy consumption is too high, the comfort degree is not uniform, and equipment loss is large are effectively solved.

The invention provides an air conditioner control method and device and an air conditioner. The control method comprises the steps that a signal for controlling the air conditioner to execute a direct blowing prevention mode is received, and the positions of indoor personnel in the indoor environment are obtained; according to the positions of the indoor people, all air outlet areas are divided into direct blowing prevention areas and normal air outlet areas, and the direct blowing prevention areas correspond to the positions of the indoor people; and the air outlet direction of the direct blowing prevention area is controlled to avoid the indoor personnel to achieve direct blowing prevention, and the air outlet state of the normal air outlet area is controlled according to the refrigeration requirement or the heating requirement of the air conditioner. According to the control method and device of the air conditioner and the air conditioner, it is guaranteed that the refrigerating effect and the heating effect of the air conditioner are not affected while the direct blowing prevention requirement of a user is met, that is, the direct blowing prevention requirement and the heating / cooling requirement of the user are met at the same time, the use experience of the user is greatly improved, and the indoor comfort level is guaranteed.

The invention discloses an air conditioner intelligent regulation and control method and device based on a comfort model. The method comprises the steps that firstly, the indoor environment temperature and the human body temperature are collected in real time through a sensor module; then, an environment comfort model to be used in a temperature interval corresponding to the indoor environment temperature is determined, and meanwhile, a human body comfort model to be used in a temperature interval corresponding to the human body temperature is determined; inputting the indoor environment temperature into a to-be-used environment comfort degree model to obtain an indoor environment comfort degree value, and inputting the indoor environment temperature and the human body temperature into a to-be-used human body comfort degree model to obtain a human body comfort degree value; and air conditioner control parameters are determined based on the indoor environment comfort level value and the human body comfort level value. The air conditioner can be controlled according to changes of the indoor environment and changes of the human body temperature of the user, and therefore the experience comfort of the user is improved.

The invention provides a vehicle comfort control method and device and a storage medium. Firstly, the target air outlet temperature of an air conditioner is determined; according to the human body comfort model, the radiation temperature when the fireplace or the seat is not started for heating is calculated, the thermal comfort evaluation index is calculated, the thermal comfort evaluation index is in the human body thermal comfort interval by adjusting the air conditioner outlet air temperature, and under the condition that the human body thermal comfort interval is maintained, the thermal comfort evaluation index is obtained. The corrected in-vehicle target head temperature after fireplace heating or seat heating is started is calculated based on the influence of heating of different gears of an in-vehicle fireplace or / and seats on the comfort degree of the human body, then the corrected air conditioner target air outlet temperature is determined, and the mapping relation between the different gears of the in-vehicle fireplace or seats and the corrected air conditioner target air outlet temperature is established. And the comfort level of the vehicle is controlled. Through cooperative control of the air conditioner and the fireplace or the seat, energy consumption of the air conditioner is reduced, and meanwhile the body surface temperature can be rapidly increased through fireplace heating or seat heating.

The invention discloses a central air conditioner control system which comprises at least one outdoor unit and at least one indoor unit in communication connection with the outdoor units through communication buses. The controllers can communicate with one another and are respectively connected with the communication bus; the temperature and humidity sensor is used for collecting indoor temperature data and humidity data; wherein the controller is used for storing current regional information, weather information and equipment operation records; the controller is also used for establishing a thermal comfort evaluation / recommendation model and an intelligent prediction schedule model by using a machine learning algorithm; the controller is further used for forming recommended control parameters according to the set requirements of the user and sending the recommended control parameters to the outdoor unit or the indoor unit. According to the invention, based on the information of the area where the user is located, the weather information, the indoor environment information and the user information, a machine learning algorithm is utilized to establish a thermal comfort evaluation recommendation model and an intelligent prediction schedule model, the models are combined to form a recommendation control strategy according to a scene set by the user, and optimal control parameters are provided for the user. And in cooperation with the energy-saving effect of the central air conditioning equipment, the purpose of efficient and energy-saving operation is achieved on the premise that the comfort level of a user is met.

The invention relates to the technical field of energy-saving control, and discloses a self-adaptive energy-saving control method and system for building heating and ventilation equipment based on digital twinning. The method comprises the steps of collecting temperature and humidity in a building, heating and ventilation equipment operation, external environment and personnel flow data, extracting energy consumption fluctuation characteristics and performing coupling analysis on the energy consumption fluctuation characteristics and equipment operation data. A distributed lag regression model is constructed in combination with external environment data, the environment influence weight and trend are calculated, and a high-dynamic region is identified through fuzzy reasoning. Energy consumption is predicted based on high-dynamic area data, a waste value is calculated, equipment operation parameters are optimized, and a target power sequence is generated through population iteration. An adjusting instruction is generated through analysis and deployed to a control system, thermal sensation feedback and instantaneous power data are collected, comfort evaluation is conducted, and the adjusting instruction is updated for optimization control. According to the method, dynamic balance of energy conservation and comfort can be realized.

To provide a presentation method enabling a user of an air conditioner to understand from what kind of viewpoint a preset temperature to be set in the air conditioner is determined.SOLUTION: A presentation method comprises a presentation step where a computer presents a first matrix in which a first axis is set as a preset temperature of an air conditioner and a second axis is set as an outside air temperature. The presentation step presents the first matrix in a mode showing the preset temperature to be set on the air conditioner and determined on the basis of a parameter related to comfort of a user of the air conditioner and the change of the parameter.SELECTED DRAWING: Figure 13

The invention discloses an environment temperature and humidity adjusting method based on the body comfort degree. The method comprises the following steps that 1, environment parameters and physiological data are collected; 2, dynamic causal diagram model analysis: constructing a dynamic causal diagram model, and identifying a key path; step 3, CFD lightweight simulation: utilizing a lightweight CFD simulation tool to generate a local eddy current model, and dynamically correcting a calculation formula of humidity; 4, sweat evaporation kinetic model calculation: calculating body surface humidity; 5, double-ring reinforcement learning framework adjustment: performing inner-ring double-ring reinforcement learning adjustment: dynamically adjusting temperature and humidity set values by taking the real-time comfort score of the user as an award, and automatically adjusting the environment temperature by double-ring reinforcement learning when the comfort level fed back by the user is reduced, and performing outer-ring double-ring reinforcement learning prediction: predicting the long-term preference of the user in combination with historical data; step 6, performing adversarial security constraint guarantee; and 7, generating and executing an adjusting instruction: generating the adjusting instruction according to the perspiration state of the user and the environmental parameters.

The utility model discloses a comfortable and breathable firefighter fire-extinguishing protective garment, and relates to the technical field of firefighter fire-extinguishing protective garments, the comfortable and breathable firefighter fire-extinguishing protective garment comprises a protective garment body, and a fabric for making the protective garment body comprises an outer flame-retardant layer, a first waterproof and breathable layer, a second waterproof and breathable layer, a heat insulation layer and a comfortable layer; the first waterproof breathable layer and the second waterproof breathable layer are made of nanofiber membranes; wherein the inner side of the outer flame-retardant layer is connected with the outer side of the first waterproof breathable layer; the inner side of the second waterproof breathable layer is connected with the outer side of the heat insulation layer; after the fabric for making the protective clothing body is sewn, the inner side of the first waterproof breathable layer is in contact with the outer side of the second waterproof breathable layer; the inner side of the heat insulation layer makes contact with the outer side of the comfortable layer. The protective clothing has the advantages of being good in air permeability and flexibility and capable of effectively improving the comfort degree of a user wearing the fire-fighting protective clothing for the firefighter.

The invention provides an adjusting control method and system for a plate radiator fan, and relates to the technical field of adjusting control, which comprises the following steps: determining the initial rotating speed of the fan by acquiring indoor temperature and a target temperature value, monitoring the temperature gradient of an air inlet and an air outlet in real time, establishing a temperature gradient prediction model, and gradually increasing the rotating speed when the predicted gradient is less than a threshold value. When the indoor temperature is larger than the upper limit, the rotating speed is gradually reduced, and the optimal rotating speed value is recorded when the indoor temperature reaches the standard. The heat dissipation efficiency can be improved, energy waste is reduced, accurate control over the indoor temperature is achieved, and the comfort level of a user is improved.

The invention discloses an outdoor thermal comfort intelligent evaluation method and system taking a psychological state as a medium. The method specifically comprises the following steps: acquiring psychological index data and multi-scale parameters, wherein the multi-scale parameters comprise environmental physical parameters and physiological response parameters; inputting the environmental physical parameters and the physiological response parameters into a dynamic feature extraction module, and performing cross-modal feature fusion through environmental physical parameter coding and physiological time sequence feature extraction to generate a fusion feature vector; constructing a psychological intermediary prediction module according to the psychological index data, inputting the fusion feature vector into the psychological intermediary prediction module, and generating a dynamic adjustment coefficient; and inputting the fusion feature vector and the dynamic adjustment coefficient into a thermal comfort evaluation module, and outputting a multi-scale prediction result. According to the method, the problem of space-time alignment of environmental parameter transient and physiological response hysteresis is solved, and the individual adaptability and prediction reliability of outdoor thermal comfort evaluation are remarkably improved.

The invention relates to the technical field of wearing control, and discloses an intelligent sleeping bag temperature and humidity self-adaptive control method and system based on multi-source data, and the method comprises the steps: obtaining multi-modal data of user physiology, environment and system state; simulating and predicting a future physiological state vector of the user through a physiological digital twin module; evaluating an energy supply capability vector of the system through an energy management twin module; the cooperative control decision module receives the two vectors and generates a final control sequence by taking minimization of physiological discomfort as a target under energy constraint; adjusting the temperature and humidity in the sleeping bag according to the sequence; the system comprises a multi-modal data acquisition module, a physiological digital twinning module, an energy management twinning module, a cooperative control decision module and a physical execution module. According to the invention, parallel prediction and evaluation of user demands and system capabilities are realized through the double twin modules, optimal balance of comfort and energy efficiency is realized through collaborative decision, and the problems of hysteresis and lack of energy consumption management of a traditional control method are solved.

The invention discloses an indoor environment monitoring method based on thermal comfort analysis, and particularly relates to the field of indoor environment industrial data processing and monitoring, which comprises the following steps: deploying a temperature and humidity sensor and a carbon dioxide sensor based on gridding, dynamically clustering a personnel dense area and migrating nodes, the Manhattan distance and random sampling consistency technology is combined to execute fault self-inspection and calibration; infrared and millimeter wave radars and a color depth camera are integrated to acquire radiation temperature and physiological data. The sensor layout is optimized through the dynamic clustering algorithm, and the fault self-inspection technology is integrated, so that the monitoring reliability of the dense area is improved; multi-modal data are aligned by using space-time diagram convolution, and privacy security is ensured in combination with a dual-encryption mechanism; the multi-region air distribution is coordinated based on the block chain to avoid conflicts; adopting reinforcement learning to construct a heat transfer model fused with physiological parameters to enhance simulation adaptability; and on the basis of regulation and control of federal learning linkage equipment, global optimization of industrial data processing of personalized thermal comfort and energy consumption balance is realized.

The invention discloses an air conditioner control method, device and equipment and a medium, and the air conditioner control method comprises the following steps: continuously collecting source parameters for air conditioner control, and inputting the source parameters into a preset neural network model to calculate a human body thermal comfort index value and a power prediction value of a current control period. Based on the data, the system can dynamically determine constraint conditions and predict a target human body thermal comfort index value in a next control period, so that energy efficiency optimization is performed under the condition that comfort is met. And the target air conditioner operation parameters are reversely solved through the genetic algorithm, so that the system can achieve optimal comfort control under the condition that the system operates at the lowest power. Intelligent and self-adaptive adjustment of the air conditioning system is achieved, the energy utilization efficiency is greatly improved, and energy consumption is reduced to the maximum extent.

The invention provides a self-adaptive assistance system and method for adjusting an exoskeleton based on a sweat sensor, and the system comprises a sweat amount detection chip which is embedded into the surface of wearable flexible clothes, is in contact with the skin of a human body, and is used for converting the detected sweat amount into an electric signal, and further converting the electric signal into a control signal; the knapsack comprises a control system, a driving system and a sensing system, and is worn on the trunk of the human body after being connected with the wearable flexible clothing; the power assisting device comprises a power assisting boot which is worn on the foot of the human body and connected with the driving system through a Bowden cable; and the specific output value of the power assisting device is controlled through the control signal. Dynamic matching of exercise intensity and mechanical assistance is achieved through an amplification control mechanism driven by sweat amount, gait phase compensation fed back by plantar pressure is combined, the energy utilization efficiency is improved by 20% or above, the problem that a traditional exoskeleton physiological-physical interface is disjointed is effectively solved, and the energy utilization rate is increased by 20% or above. The assisting synchronism is improved, and meanwhile the wearing comfort degree and the motion freedom degree are remarkably improved.

The invention discloses an air conditioner condensation prevention control method, device and system and an air conditioner, and belongs to the field of air conditioner condensation prevention. The air conditioner anti-condensation control method comprises the steps that after target parameters are obtained, a target threshold value is obtained through calculation based on the target parameters; and when the temperature difference value of the evaporator surface temperature and the dew point temperature is smaller than the target threshold value, anti-condensation control is conducted. According to the scheme, the threshold value is dynamically determined based on the indoor air humidity and the operation frequency of the compressor, and the threshold value is substantially the allowable minimum temperature difference safety boundary between the evaporator surface temperature and the dew point temperature. When it is monitored that the actual temperature difference between the two is lower than the dynamic threshold value, the controller automatically starts an anti-condensation measure; condensation can be prevented in time, condensation and water dripping of the indoor unit are prevented, the comfort level of a user can be guaranteed as much as possible, and the user experience is improved.

The invention relates to the technical field of heating systems, and particularly discloses a seat cushion heating integrated intelligent control heating system which comprises a user sensing module, a temperature control module, an energy consumption management module, a safety protection module, a communication interaction module and a self-checking diagnosis module. The user sensing module collects comprehensive data through a multi-source sensor group and generates a user habit feature set, and the temperature control module dynamically adjusts the working mode of a heating area according to the data and real-time environment parameters, so that personalized heating is realized; for example, according to sitting postures and physiological characteristics of different users, appropriate temperatures are provided for different temperature zones, the comfort level of the users is improved, a safety protection module compares an energy consumption evaluation index with a preset safety threshold value, a multi-stage protection strategy is implemented, and when the energy consumption evaluation index is too high, measures such as power reduction, partition power failure and forced heat dissipation are taken, so that the safety of the users is improved. The temperature of the seat cushion is effectively prevented from being too high, and safety accidents such as user scalding and fire disasters are avoided.