Intelligent building heating management system based on indoor environment parameter adjustment

Abstract

This invention relates to a smart building heating management system based on indoor environmental parameter adjustment, and relates to the field of smart building and HVAC intelligent control technology. It includes a multi-dimensional environmental perception unit, a dynamic comfort assessment unit, a residual heat prediction unit, and a compensation and coordination control unit. This invention dynamically generates a target comfort temperature zone by introducing a PMV evaluation model and integrating relative humidity, average radiant temperature, and real-time occupant status data. While ensuring the actual comfort of indoor occupants, it avoids energy waste. By establishing a building thermal damping model, it quantifies the dynamic residual heat accumulated inside in real time. When the residual heat at the bottom layer meets the theoretical requirement to push the room temperature to the upper limit, it outputs a valve adjustment command in advance, utilizing the flywheel effect to complete the residual heat heating, significantly reducing redundant energy consumption. Simultaneously, it predicts and issues an opening command before the room temperature falls below the comfort zone, ensuring a seamless connection between the new round of heat transfer and indoor cooling loss.

Description

Technical Field

[0001] This invention relates to the field of smart building and HVAC intelligent control technology, and in particular to a smart building heating management system based on indoor environmental parameter adjustment. Background Technology

[0002] Existing smart heating systems mostly employ a single room temperature negative feedback control logic (such as PID control). In practical applications (especially in high-heat-inertia terminals like underfloor heating), the following two problems still exist:

[0003] Due to the significant thermal inertia of the building envelope and the large-capacity heating medium, when the sensor detects that the room temperature is below the set threshold (e.g., 21°C), the system starts full-load heating. However, there is a 2-4 hour lag in heat transfer, causing the room temperature to continue to drop (under-adjustment). When the room temperature reaches 21°C and the valve is shut off, the residual heat inside the heat storage medium continues to be released into the room, causing a significant overshoot in room temperature (e.g., rising above 25°C), resulting in serious energy waste and discomfort from alternating hot and cold temperatures.

[0004] Furthermore, the existing control only targets air temperature as an absolute factor, without comprehensively considering environmental factors such as indoor humidity, average radiant temperature, indoor occupant density and activity status, resulting in the problem that the temperature meets the standard but people feel uncomfortable.

[0005] To address the aforementioned technical deficiencies, a solution is proposed. Summary of the Invention

[0006] The purpose of this invention is to dynamically generate a target comfort temperature zone by introducing a PMV evaluation model and integrating relative humidity, average radiant temperature, and real-time personnel status data. This ensures the actual comfort of indoor personnel while avoiding energy waste caused by blindly pursuing fixed room temperature settings. By establishing a building thermal damping model, the dynamic residual heat accumulated inside is quantified in real time. When the residual heat at the bottom layer has met the theoretical requirement of pushing the room temperature to the upper limit, a valve adjustment command is output in advance to complete the residual heat heating using the flywheel effect, significantly reducing redundant energy consumption. At the same time, the opening command is predicted and issued in advance before the room temperature drops below the comfort zone, so that the new round of heat transfer and indoor cold loss are seamlessly connected, eliminating the waiting period of personnel freezing in traditional control and achieving a smooth and constant indoor thermal environment.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a smart building heating management system based on indoor environmental parameter adjustment, comprising a multi-dimensional environmental sensing unit, a dynamic comfort assessment unit, a residual heat prediction unit, and a compensation and coordination control unit, wherein:

[0008] The multi-dimensional environmental sensing unit is used to collect real-time data on indoor air temperature, relative humidity, average radiant temperature, and personnel status in smart buildings, and integrate them into multi-modal environmental parameters.

[0009] The dynamic comfort assessment unit is connected to the multi-dimensional environmental perception to obtain multi-modal environmental parameters. It is used to calculate the predicted average number of votes based on the indoor air temperature, relative humidity and average radiant temperature, and combined with the personnel status data, dynamically generate the upper limit threshold and lower limit threshold of the target comfort temperature zone.

[0010] The residual heat prediction unit is used to obtain the supply water temperature, return water temperature and terminal surface temperature of the heating terminal, establish a building thermal damping model, and calculate in real time the dynamic residual heat accumulated in the current heating medium and heat storage structure, as well as the lag time of heat transfer from the heat source to the indoor surface.

[0011] The compensation and coordination control unit is connected to the dynamic comfort assessment unit and the residual heat prediction unit respectively. It is used to calculate the theoretical compensation heat required to raise the current indoor air temperature to the upper limit threshold. When it is determined that the dynamic residual heat is greater than the theoretical compensation heat, it outputs a command to reduce the opening degree of the heating valve.

[0012] Based on the indoor cooling load loss rate, the remaining time for the room temperature to drop to the lower limit threshold is predicted. When it is determined that the remaining time is less than or equal to the lag time, a heating start command is output in advance.

[0013] Furthermore, the specific process for obtaining multimodal environmental parameters is as follows:

[0014] By deploying distributed sensor arrays at different spatial nodes indoors, multimodal environmental parameters are acquired synchronously. Specifically:

[0015] By deploying integrated temperature and humidity sensors at standard breathing height indoors and avoiding areas exposed to cold or heat sources and direct sunlight, the air temperature and relative humidity inside smart buildings can be collected in real time.

[0016] The intensity of radiative heat exchange in the building envelope is monitored in real time by a black ball thermometer installed in the center of the interior wall in order to calculate the average radiative temperature.

[0017] The infrared pyroelectric array captures the boundary of human body thermal radiation to count the number and density of people, while millimeter-wave radar is used to detect the spatial coordinates and minute movements of people to quantify the status data of people.

[0018] Based on the built-in data processing microcontroller unit, a unified timestamp is added to the underlying data transmitted back by the distributed sensor group. Based on the device MAC address and the indoor BIM coordinate system, spatial location is mapped, and the air temperature, relative humidity, average radiation temperature and personnel status data are processed into time series data by dimensionless processing.

[0019] Kalman filtering is used to smooth the time-series data and remove abnormal noise caused by transient interference. The cleaned and aligned air temperature, relative humidity, mean radiation temperature and personnel status data are then spliced ​​and packaged according to a preset communication protocol to construct a structured multidimensional feature vector, which is periodically sent to the dynamic comfort assessment unit.

[0020] Furthermore, the specific process for generating the upper and lower threshold values ​​of the target comfort temperature zone is as follows:

[0021] Based on the pre-stored human thermal balance evaluation model, under the premise of preset thermal resistance of standard clothing and basal resting metabolic rate in the current season, the collected air temperature, relative humidity and average radiation temperature are input into the human thermal balance evaluation model. Through the preset interpolation mapping network, the predicted average vote number under the current purely objective physical environment is output. The predicted average vote number is used to objectively quantify the cold and heat perception tendency of most people under the current environment.

[0022] Acquire personnel status data, extract the number and density of personnel in the current space, as well as the spatial coordinates and minor movements of personnel, to determine the activity level of indoor personnel. When the personnel activity status tends to be active, according to the change gradient of personnel status data, retrieve the preset personnel thermal gain compensation parameter, and introduce the personnel thermal gain compensation parameter as a feedforward adjustment variable into the human body thermal balance evaluation model to offset part of the required external environmental heat supply.

[0023] A preset standard PMV neutral target range is obtained, which is used to indicate that the human body has no obvious discomfort from cold or heat. When the detected human density is greater than the preset density threshold, it is determined that a low room temperature is needed to maintain the thermal balance of the human body.

[0024] When the detected population density is less than or equal to the preset density threshold, it is determined that a high room temperature is required to maintain the thermal balance of the human body.

[0025] The upper and lower boundaries of the standard PMV neutral range are reconstructed into the corresponding physical absolute values ​​of air temperature while keeping the current relative humidity and mean radiation temperature constant. The minimum value of the physical absolute value of air temperature is locked as the lower limit threshold of the target comfort temperature zone, and the maximum value of the physical absolute value of air temperature is locked as the upper limit threshold of the target comfort temperature zone.

[0026] Furthermore, the specific process of establishing the building thermal damping model is as follows:

[0027] Based on the actual physical structure of the heating terminal of a smart building, the complete process of heat transfer from the heat source to the indoor air is divided into multiple sequentially connected heat dissipation nodes. The heat dissipation nodes include heating medium nodes, pipe and contact surface nodes, heat storage structure layer nodes, terminal finishing layer nodes, and indoor air nodes.

[0028] The thermal conductivity, laying thickness and effective heat transfer area of ​​each structural layer of a smart building are obtained, and the impedance of the structural layer to heat flow is mapped into an equivalent thermal resistance damping parameter. The equivalent thermal resistance damping parameter is used to represent the energy attenuation rate of heat flow when it is transferred across layers.

[0029] The material density, specific heat capacity, and total volume of each structural layer of a smart building are obtained, and the heat storage capacity of the structural layer is mapped to an equivalent heat capacity energy storage parameter. The equivalent heat capacity energy storage parameter is used to represent the absorption and temporary storage capacity of heat along the transfer path.

[0030] The thermal resistance damping parameter and the thermal capacity storage parameter are combined in series and parallel topology according to the heat transfer physical order to construct a lumped parameter form of thermal damping network model. The thermal damping network model is used to represent the dual dynamic response process when heat is input into the heating medium. That is, part of the heat is absorbed and temporarily stored by the thermal capacity storage parameter, forming heat accumulation inside the medium, and the other part of the heat overcomes the thermal resistance damping and is transferred to the next node step by step.

[0031] Dynamic boundary conditions are loaded into the completed thermal damping network model. The energy input end of the thermal damping network model uses the real-time monitored supply and return water temperature difference and circulation flow rate as the heat injection boundary, while the energy output end is decoupled into a two-way heat exchange boundary: one is the effective output boundary for convection and radiation heat exchange from the heating terminal surface to the indoor air, and the other is the base heat loss damping boundary for heat storage structure to conduct heat to the foundation and the lower space.

[0032] After the thermal damping network model is constructed, online iterative correction is performed using real-time collected supply and return water temperatures, terminal surface temperatures, and air temperatures. By comparing the timing of the heating valve action with the actual response time of the terminal surface temperature and the actual slope of the surface temperature rise, the equivalent thermal resistance and effective heat capacity parameters in the thermal damping network model are adaptively fine-tuned.

[0033] Furthermore, the specific process for calculating the dynamic residual heat is as follows:

[0034] The dynamic residual heat refers to the net heat that has been removed from the heating circulation water at the current moment, but has not yet been dissipated into the indoor air, and is instead retained and accumulated inside the structural layers of the building.

[0035] Based on the preset sampling period, the dynamic residual heat accumulated in each structural layer is calculated according to the law of conservation of energy. The calculation formula is as follows:

[0036] ;

[0037] In the formula:

[0038] t represents the start time of this heating cycle, and t represents the current time.

[0039] The specific heat capacity of the heating medium. For the real-time quality flow rate of the heating network;

[0040] The water supply temperature at the heating terminal. This refers to the return water temperature at the heating terminal.

[0041] h is the overall heat transfer coefficient between the heating terminal surface and the indoor air;

[0042] A represents the effective heat dissipation area of ​​the heating terminal;

[0043] The surface temperature of the heating terminal. This refers to the indoor air temperature.

[0044] Furthermore, the specific process for calculating the lag time is as follows:

[0045] The lag time refers to the time difference between a change in the heating source and a corresponding response in the surface temperature of the heating terminal.

[0046] The lag time is calculated by combining theoretical property formulas with real-time data calibration. The specific calculation formula is as follows:

[0047] ;

[0048] In the formula:

[0049] The characteristic thickness for heat transfer is specifically the vertical distance from the pipe wall to the floor surface;

[0050] The overall thermal diffusivity of the heat storage structure is...

[0051] Specifically ,in The thermal conductivity is preset. Where c is the density and c is the specific heat capacity of the material;

[0052] These are the preset dynamic calibration coefficients;

[0053] The actual time difference between the time of instruction issuance and the change in the preset temperature gradient of the surface temperature of the heating terminal is periodically extracted from historical operation data. The calculated lag time is iteratively corrected by Kalman filtering algorithm, and the dynamic calibration coefficient is updated in real time to obtain accurate lag time.

[0054] Furthermore, establish the comprehensive indoor heat capacity. And calculate the static heat demand based on the overall indoor heat capacity. The specific calculation formula is as follows:

[0055] ;

[0056] ;

[0057] In the formula:

[0058] The upper limit threshold is generated in real time by the dynamic comfort assessment unit;

[0059] This refers to the real-time indoor air temperature.

[0060] The indoor total heat capacity;

[0061] , , These are the density, specific heat capacity, and volume of indoor air, respectively.

[0062] , The mass and specific heat capacity of each equivalent indoor heat-absorbing component.

[0063] Obtain the indoor cooling load loss rate and the hysteresis time of heat transfer Calculate dynamic cooling loss heat The specific calculation formula is as follows:

[0064] ;

[0065] in:

[0066] In order to delay time The average indoor cooling load loss rate is dynamically affected by the indoor-outdoor temperature difference.

[0067] The lag time for residual heat to transfer from the heat source to the indoor surface;

[0068] static heat demand With dynamic cooling loss heat By performing superposition and synthesis, the final theoretical heat compensation is obtained. The specific calculation formula is as follows:

[0069] ;

[0070] When the dynamic residual heat is greater than the theoretical compensation heat, it is determined that the heat accumulated at the bottom layer has met the heating demand, and a command to reduce the opening degree of the heating valve is output.

[0071] Furthermore, the specific process for predicting the remaining time for the room temperature to drop to the lower limit threshold based on the indoor cooling load loss rate is as follows:

[0072] Real-time acquisition of current indoor air temperature Outdoor ambient temperature and the lower limit threshold value output by the dynamic comfort assessment unit. And obtain the total heat transfer coefficient of the current building space. The total heat transfer coefficient includes heat conduction loss from the building envelope (walls, doors and windows) and heat loss from infiltration ventilation;

[0073] Calculate the indoor cooling load loss rate at current time t based on Newton's law of cooling. The specific calculation formula is as follows:

[0074] ;

[0075] in, This refers to the real-time indoor air temperature.

[0076] The total heat storage capacity of indoor air and the shallow building envelope involved in heat exchange is obtained, and the total heat storage capacity is defined as the building's equivalent heat capacity. It satisfies the following energy conservation differential equation:

[0077] ;

[0078] Will Integrating, with the current time t=0 as the initial condition, at this time... = Solve for the threshold value when the temperature drops to the lower limit. Remaining time required The integration process is as follows:

[0079] ;

[0080] Calculate the remaining time :

[0081] .

[0082] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0083] This intelligent building heating management system, based on indoor environmental parameter adjustment, dynamically generates target comfort temperature zones by introducing a PMV evaluation model and integrating relative humidity, average radiant temperature, and real-time occupant status data. While ensuring the actual comfort of indoor occupants, it avoids energy waste caused by blindly pursuing fixed room temperature setpoints. By establishing a building thermal damping model, it quantifies the dynamic residual heat accumulated inside in real time. When the residual heat at the bottom layer has met the theoretical requirement to push the room temperature to the upper limit, it outputs valve adjustment commands in advance, using the flywheel effect to complete the residual heat heating, significantly reducing redundant energy consumption. At the same time, it predicts and issues start-up commands in advance before the room temperature drops below the comfort zone, so that the new round of heat transfer and indoor cold loss are seamlessly connected, eliminating the waiting period of occupants being cold in traditional control, and achieving a smooth and constant indoor thermal environment. Attached Figure Description

[0084] Figure 1 A schematic diagram of the overall structure of the present invention is shown;

[0085] Figure 2 A schematic diagram of the overall method flow of the present invention is shown. Detailed Implementation

[0086] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0087] Example:

[0088] like Figure 1-2 As shown, the intelligent building heating management system based on indoor environmental parameter adjustment includes a multi-dimensional environmental sensing unit, a dynamic comfort assessment unit, a residual heat prediction unit, and a compensation and coordination control unit, wherein:

[0089] The multi-dimensional environmental sensing unit is used to collect real-time data on indoor air temperature, relative humidity, average radiant temperature, and personnel status in smart buildings, and integrate them into multi-modal environmental parameters.

[0090] The specific process for obtaining multimodal environmental parameters is as follows:

[0091] By deploying distributed sensor arrays at different spatial nodes indoors, multimodal environmental parameters are acquired synchronously. Specifically:

[0092] By deploying integrated temperature and humidity sensors at standard breathing height indoors and avoiding areas exposed to cold or heat sources and direct sunlight, the air temperature and relative humidity inside smart buildings can be collected in real time.

[0093] By using a black ball thermometer installed in the center of the indoor wall, the intensity of radiative heat exchange of the building envelope, such as walls, ground and windows, can be sensed in real time to calculate the average radiative temperature.

[0094] The infrared pyroelectric array captures the thermal radiation boundary of the human body to count the number and density of people. At the same time, millimeter-wave radar is used to detect the spatial coordinates and minute movements of people (such as sitting still or pacing) to quantify the status data of people.

[0095] Based on the built-in data processing microcontroller unit, a unified timestamp is added to the underlying data transmitted back by the distributed sensor group. Based on the device MAC address and the indoor BIM coordinate system, spatial location is mapped, and the air temperature, relative humidity, average radiation temperature and personnel status data are processed into time series data by dimensionless processing.

[0096] Kalman filtering is used to smooth the time-series data and remove abnormal noise caused by transient interference (such as sudden changes in local temperature and humidity caused by opening and closing doors, and false alarms about personnel status caused by pet activities). After cleaning and aligning the air temperature, relative humidity, average radiation temperature, and personnel status data, they are spliced ​​and packaged according to the preset communication protocol to construct a structured multidimensional feature vector, which is periodically sent to the dynamic comfort assessment unit.

[0097] The dynamic comfort assessment unit is connected to the multi-dimensional environmental perception to obtain multi-modal environmental parameters. It is used to calculate the predicted average number of votes based on the indoor air temperature, relative humidity and average radiant temperature, and combined with the personnel status data, dynamically generate the upper limit threshold and lower limit threshold of the target comfort temperature zone.

[0098] The specific process for generating the upper and lower threshold values ​​for the target comfort temperature zone is as follows:

[0099] Based on a pre-stored human thermal balance evaluation model, under the premise of preset thermal resistance of standard clothing in the current season and basal resting metabolic rate, the collected air temperature (characterizing the intensity of convective heat transfer), relative humidity (characterizing the degree of obstruction of heat dissipation by human sweat evaporation) and average radiant temperature (characterizing the amount of radiative heat exchange between the human body and walls and the ground) are input into the human thermal balance evaluation model. Through a preset interpolation mapping network, the predicted average vote number under the current purely objective physical environment is output. The predicted average vote number is used to objectively quantify the cold and heat perception tendency of most people under the current environment.

[0100] The system acquires personnel status data, extracts the number and density of people in the current space, as well as their spatial coordinates and minor movements, to determine the activity level of people indoors. When the activity level of people tends to be active (such as people indoors changing from sitting to walking), the system retrieves the preset personnel thermal gain compensation parameter based on the gradient of the personnel status data. The personnel thermal gain compensation parameter is then introduced into the human body thermal balance evaluation model as a feedforward adjustment variable to offset part of the required external environmental heat supply.

[0101] A preset standard PMV neutral target range is obtained, which is used to indicate that the human body has no obvious discomfort from cold or heat. When the detected human density is greater than the preset density threshold, it is determined that a low room temperature is needed to maintain the thermal balance of the human body.

[0102] When the detected population density is less than or equal to the preset density threshold, it is determined that a high room temperature is required to maintain the thermal balance of the human body.

[0103] The upper and lower boundaries of the standard PMV neutral range are reconstructed into the corresponding physical absolute values ​​of air temperature while keeping the current relative humidity and mean radiation temperature constant. The minimum value of the physical absolute value of air temperature is locked as the lower limit threshold of the target comfort temperature zone, and the maximum value of the physical absolute value of air temperature is locked as the upper limit threshold of the target comfort temperature zone.

[0104] The residual heat prediction unit is used to obtain the supply water temperature, return water temperature and terminal surface temperature of the heating terminal, establish a building thermal damping model, and calculate in real time the dynamic residual heat accumulated in the current heating medium and heat storage structure, as well as the lag time of heat transfer from the heat source to the indoor surface.

[0105] The specific process of establishing a building thermal damping model is as follows:

[0106] Based on the actual physical structure of the heating terminal of a smart building, the complete process of heat transfer from the heat source to the indoor air is divided into multiple sequentially connected thermal nodes. The thermal nodes include heating medium nodes (such as circulating water in the coil), pipe and contact surface nodes, heat storage structure layer nodes (such as concrete backfill layer, moisture-proof layer and pad layer), terminal finishing layer nodes (such as the surface of ceramic tile or wood flooring) and indoor air nodes.

[0107] The thermal conductivity, laying thickness and effective heat transfer area of ​​each structural layer of a smart building are obtained, and the impedance of the structural layer to heat flow is mapped into an equivalent thermal resistance damping parameter. The equivalent thermal resistance damping parameter is used to represent the energy attenuation rate of heat flow when it is transferred across layers.

[0108] The material density, specific heat capacity, and total volume of each structural layer of a smart building are obtained, and the heat storage capacity of the structural layer is mapped to an equivalent heat capacity energy storage parameter. The equivalent heat capacity energy storage parameter is used to represent the absorption and temporary storage capacity of heat along the transfer path.

[0109] The thermal resistance damping parameter and the thermal capacity storage parameter are combined in series and parallel topology according to the heat transfer physical order to construct a lumped parameter form of thermal damping network model. The thermal damping network model is used to represent the dual dynamic response process when heat is input into the heating medium. That is, part of the heat is absorbed and temporarily stored by the thermal capacity storage parameter, forming heat accumulation inside the medium, and the other part of the heat overcomes the thermal resistance damping and is transferred to the next node step by step.

[0110] Dynamic boundary conditions are loaded into the completed thermal damping network model. The energy input end of the thermal damping network model uses the real-time monitored supply and return water temperature difference and circulation flow rate as the heat injection boundary, while the energy output end is decoupled into a two-way heat exchange boundary: one is the effective output boundary for convection and radiation heat exchange from the heating terminal surface to the indoor air, and the other is the base heat loss damping boundary for heat storage structure to conduct heat to the foundation and the lower space.

[0111] After the thermal damping network model is constructed, online iterative correction is performed using real-time collected supply and return water temperatures, terminal surface temperatures, and air temperatures. By comparing the timing of the heating valve action with the actual response time of the terminal surface temperature and the actual slope of the surface temperature rise, the equivalent thermal resistance and effective heat capacity parameters in the thermal damping network model are adaptively fine-tuned.

[0112] The specific process for calculating dynamic residual heat is as follows:

[0113] The dynamic residual heat refers to the net heat that has been removed from the heating circulation water at the current moment, but has not yet been dissipated into the indoor air, and is instead retained and accumulated inside the various structural layers of the building (such as the foundation backfill layer);

[0114] Based on the preset sampling period, the dynamic residual heat accumulated in each structural layer is calculated according to the law of conservation of energy. The calculation formula is as follows:

[0115]

[0116] In the formula:

[0117] t represents the start time of this heating cycle, and t represents the current time.

[0118] The specific heat capacity of the heating medium. For the real-time quality flow rate of the heating network;

[0119] The water supply temperature at the heating terminal. This refers to the return water temperature at the heating terminal.

[0120] h is the combined heat transfer coefficient between the heating terminal surface and the indoor air (including convective and radiative heat transfer coefficients).

[0121] A represents the effective heat dissipation area of ​​the heating terminal;

[0122] The surface temperature of the heating terminal. This refers to the indoor air temperature.

[0123] The specific process for calculating the lag time is as follows:

[0124] The lag time refers to the time difference between a change in the heating source (water temperature or flow rate) and a corresponding response in the surface temperature of the heating terminal.

[0125] The lag time is calculated by combining theoretical property formulas with real-time data calibration. The specific calculation formula is as follows:

[0126] ;

[0127] In the formula:

[0128] The characteristic thickness for heat transfer is specifically the vertical distance from the pipe wall to the floor surface;

[0129] The overall thermal diffusivity of the heat storage structure is...

[0130] Specifically ,in The thermal conductivity is preset. Where c is the density and c is the specific heat capacity of the material;

[0131] These are the preset dynamic calibration coefficients;

[0132] The actual time difference between the time of instruction issuance and the change in the preset temperature gradient of the surface temperature of the heating terminal is periodically extracted from historical operation data. The calculated lag time is iteratively corrected by Kalman filtering algorithm, and the dynamic calibration coefficient is updated in real time to obtain accurate lag time.

[0133] The compensation and coordination control unit is connected to the dynamic comfort assessment unit and the residual heat prediction unit respectively. It is used to calculate the theoretical compensation heat required to raise the current indoor air temperature to the upper limit threshold. When it is determined that the dynamic residual heat is greater than the theoretical compensation heat, it outputs a command to reduce the opening degree of the heating valve.

[0134] Based on the indoor cooling load loss rate, the remaining time for the room temperature to drop to the lower limit threshold is predicted. When it is determined that the remaining time is less than or equal to the lag time, a heating start command is output in advance.

[0135] Establish indoor comprehensive heat capacity And calculate the static heat demand based on the overall indoor heat capacity. The specific calculation formula is as follows:

[0136]

[0137]

[0138] In the formula:

[0139] The upper limit threshold is generated in real time by the dynamic comfort assessment unit;

[0140] This refers to the real-time indoor air temperature.

[0141] The indoor total heat capacity;

[0142] , , These are the density, specific heat capacity, and volume of indoor air, respectively.

[0143] , The mass and specific heat capacity of each equivalent indoor heat-absorbing component (furniture, wall).

[0144] Obtain the indoor cooling load loss rate (Heat loss per unit time) and the lag time of heat transfer Calculate dynamic cooling loss heat The specific calculation formula is as follows:

[0145]

[0146] in:

[0147] In order to delay time The average indoor cooling load loss rate is dynamically affected by the indoor-outdoor temperature difference.

[0148] The lag time for residual heat to transfer from the heat source to the indoor surface;

[0149] static heat demand With dynamic cooling loss heat By performing superposition and synthesis, the final theoretical heat compensation is obtained. The specific calculation formula is as follows:

[0150]

[0151] When the dynamic residual heat is greater than the theoretical compensation heat, it is determined that the heat accumulated at the bottom layer has met the heating demand, and a command to reduce the opening degree of the heating valve is output.

[0152] The specific process for predicting the remaining time for the room temperature to drop to the lower threshold value based on the indoor cooling load loss rate is as follows:

[0153] Real-time acquisition of current indoor air temperature Outdoor ambient temperature and the lower limit threshold value output by the dynamic comfort assessment unit. And obtain the total heat transfer coefficient of the current building space. The total heat transfer coefficient includes heat conduction loss from the building envelope (walls, doors and windows) and heat loss from infiltration ventilation;

[0154] Calculate the indoor cooling load loss rate at current time t based on Newton's law of cooling. The specific calculation formula is as follows:

[0155] ;

[0156] in, This refers to the real-time indoor air temperature.

[0157] The total heat storage capacity of indoor air and the shallow building envelope involved in heat exchange is obtained, and the total heat storage capacity is defined as the building's equivalent heat capacity. During the period when the heating valve is in its regulating margin, the decrease in indoor temperature is offset by the heat released by the heat capacity and the loss of cooling load, satisfying the following energy conservation differential equation:

[0158] ;

[0159] Will Integrating, with the current time t=0 as the initial condition, at this time... = Solve for the threshold value when the temperature drops to the lower limit. Remaining time required The integration process is as follows:

[0160]

[0161] Calculate the remaining time :

[0162]

[0163] When the remaining time is less than or equal to the lag time, it indicates that if heating is not turned on immediately, the room temperature will inevitably drop below the lower limit of the target comfort temperature zone before the heat reaches the indoor surface. The heating start command is output in advance, and the time difference is used to eliminate the temperature drop overshoot caused by thermal inertia, so as to achieve a smooth transition of indoor temperature within the comfort temperature zone.

[0164] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value.

[0165] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0166] In the two embodiments provided in this application, it should be understood that the disclosed apparatus and system can be implemented in other ways; for example, the apparatus embodiments described above are merely illustrative, and the division of modules is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed; another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the apparatus or module can be electrical, mechanical or other forms.

[0167] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A smart building heating management system based on indoor environmental parameter adjustment, characterized in that, It includes a multi-dimensional environmental sensing unit, a dynamic comfort assessment unit, a residual heat prediction unit, and a compensation and coordination control unit, wherein: The multi-dimensional environmental sensing unit is used to collect real-time data on indoor air temperature, relative humidity, average radiant temperature, and personnel status in smart buildings, and integrate them into multi-modal environmental parameters. The dynamic comfort assessment unit is connected to the multi-dimensional environmental perception to obtain multi-modal environmental parameters. It is used to calculate the predicted average number of votes based on the indoor air temperature, relative humidity and average radiant temperature, and combined with the personnel status data, dynamically generate the upper limit threshold and lower limit threshold of the target comfort temperature zone. The residual heat prediction unit is used to obtain the supply water temperature, return water temperature and terminal surface temperature of the heating terminal, establish a building thermal damping model, and calculate in real time the dynamic residual heat accumulated in the current heating medium and heat storage structure, as well as the lag time of heat transfer from the heat source to the indoor surface. The compensation and coordination control unit is connected to the dynamic comfort assessment unit and the residual heat prediction unit respectively. It is used to calculate the theoretical compensation heat required to raise the current indoor air temperature to the upper limit threshold. When it is determined that the dynamic residual heat is greater than the theoretical compensation heat, it outputs a command to reduce the opening degree of the heating valve. Based on the indoor cooling load loss rate, the remaining time for the room temperature to drop to the lower limit threshold is predicted. When it is determined that the remaining time is less than or equal to the lag time, a heating start command is output in advance.

2. The intelligent building heating management system based on indoor environmental parameter adjustment according to claim 1, characterized in that, The specific process for obtaining multimodal environmental parameters is as follows: By deploying distributed sensor arrays at different spatial nodes indoors, multimodal environmental parameters are acquired synchronously. Specifically: By deploying integrated temperature and humidity sensors at standard breathing height indoors and avoiding areas exposed to cold or heat sources and direct sunlight, the air temperature and relative humidity inside smart buildings can be collected in real time. The intensity of radiative heat exchange in the building envelope is monitored in real time by a black ball thermometer installed in the center of the interior wall in order to calculate the average radiative temperature. The infrared pyroelectric array captures the boundary of human body thermal radiation to count the number and density of people, while millimeter-wave radar is used to detect the spatial coordinates and minute movements of people to quantify the status data of people. Based on the built-in data processing microcontroller unit, a unified timestamp is added to the underlying data transmitted back by the distributed sensor group. Based on the device MAC address and the indoor BIM coordinate system, spatial location is mapped, and the air temperature, relative humidity, average radiation temperature and personnel status data are processed into time series data by dimensionless processing. Kalman filtering is used to smooth the time-series data and remove abnormal noise caused by transient interference. The cleaned and aligned air temperature, relative humidity, mean radiation temperature and personnel status data are then spliced ​​and packaged according to a preset communication protocol to construct a structured multidimensional feature vector, which is periodically sent to the dynamic comfort assessment unit.

3. The intelligent building heating management system based on indoor environmental parameter adjustment according to claim 1, characterized in that, The specific process for generating the upper and lower threshold values ​​for the target comfort temperature zone is as follows: Based on the pre-stored human thermal balance evaluation model, under the premise of preset thermal resistance of standard clothing and basal resting metabolic rate in the current season, the collected air temperature, relative humidity and average radiation temperature are input into the human thermal balance evaluation model. Through the preset interpolation mapping network, the predicted average vote number under the current purely objective physical environment is output. The predicted average vote number is used to objectively quantify the cold and heat perception tendency of most people under the current environment. Acquire personnel status data, extract the number and density of personnel in the current space, as well as the spatial coordinates and minor movements of personnel, to determine the activity level of indoor personnel. When the personnel activity status tends to be active, according to the change gradient of personnel status data, retrieve the preset personnel thermal gain compensation parameter, and introduce the personnel thermal gain compensation parameter as a feedforward adjustment variable into the human body thermal balance evaluation model to offset part of the required external environmental heat supply. A preset standard PMV neutral target range is obtained, which is used to indicate that the human body has no obvious discomfort from cold or heat. When the detected human density is greater than the preset density threshold, it is determined that a low room temperature is needed to maintain the thermal balance of the human body. When the detected population density is less than or equal to the preset density threshold, it is determined that a high room temperature is required to maintain the thermal balance of the human body. The upper and lower boundaries of the standard PMV neutral range are reconstructed into the corresponding physical absolute values ​​of air temperature while keeping the current relative humidity and mean radiation temperature constant. The minimum value of the physical absolute value of air temperature is locked as the lower limit threshold of the target comfort temperature zone, and the maximum value of the physical absolute value of air temperature is locked as the upper limit threshold of the target comfort temperature zone.

4. The intelligent building heating management system based on indoor environmental parameter adjustment according to claim 1, characterized in that, The specific process of establishing a building thermal damping model is as follows: Based on the actual physical structure of the heating terminal of a smart building, the complete process of heat transfer from the heat source to the indoor air is divided into multiple sequentially connected heat dissipation nodes. The heat dissipation nodes include heating medium nodes, pipe and contact surface nodes, heat storage structure layer nodes, terminal finishing layer nodes, and indoor air nodes. The thermal conductivity, laying thickness and effective heat transfer area of ​​each structural layer of a smart building are obtained, and the impedance of the structural layer to heat flow is mapped into an equivalent thermal resistance damping parameter. The equivalent thermal resistance damping parameter is used to represent the energy attenuation rate of heat flow when it is transferred across layers. The material density, specific heat capacity, and total volume of each structural layer of a smart building are obtained, and the heat storage capacity of the structural layer is mapped to an equivalent heat capacity energy storage parameter. The equivalent heat capacity energy storage parameter is used to represent the absorption and temporary storage capacity of heat along the transfer path. The thermal resistance damping parameter and the thermal capacity storage parameter are combined in series and parallel topology according to the heat transfer physical order to construct a lumped parameter form of thermal damping network model. The thermal damping network model is used to represent the dual dynamic response process when heat is input into the heating medium. That is, part of the heat is absorbed and temporarily stored by the thermal capacity storage parameter, forming heat accumulation inside the medium, and the other part of the heat overcomes the thermal resistance damping and is transferred to the next node step by step. Dynamic boundary conditions are loaded into the completed thermal damping network model. The energy input end of the thermal damping network model uses the real-time monitored supply and return water temperature difference and circulation flow rate as the heat injection boundary, while the energy output end is decoupled into a two-way heat exchange boundary: one is the effective output boundary for convection and radiation heat exchange from the heating terminal surface to the indoor air, and the other is the base heat loss damping boundary for heat storage structure to conduct heat to the foundation and the lower space. After the thermal damping network model is constructed, online iterative correction is performed using real-time collected supply and return water temperatures, terminal surface temperatures, and air temperatures. By comparing the timing of the heating valve action with the actual response time of the terminal surface temperature and the actual slope of the surface temperature rise, the equivalent thermal resistance and effective heat capacity parameters in the thermal damping network model are adaptively fine-tuned.

5. The intelligent building heating management system based on indoor environmental parameter adjustment according to claim 1, characterized in that, The specific process for calculating dynamic residual heat is as follows: The dynamic residual heat refers to the net heat that has been removed from the heating circulation water at the current moment, but has not yet been dissipated into the indoor air, and is instead retained and accumulated inside the structural layers of the building. Based on the preset sampling period, the dynamic residual heat accumulated in each structural layer is calculated according to the law of conservation of energy. The calculation formula is as follows: ； In the formula: t represents the start time of this heating cycle, and t represents the current time. The specific heat capacity of the heating medium. For the real-time quality flow rate of the heating network; The water supply temperature at the heating terminal. This refers to the return water temperature at the heating terminal. h is the overall heat transfer coefficient between the heating terminal surface and the indoor air; A represents the effective heat dissipation area of ​​the heating terminal; The surface temperature of the heating terminal. This refers to the indoor air temperature.

6. The intelligent building heating management system based on indoor environmental parameter adjustment according to claim 1, characterized in that, The specific process for calculating the lag time is as follows: The lag time refers to the time difference between a change in the heating source and a corresponding response in the surface temperature of the heating terminal. The lag time is calculated by combining theoretical property formulas with real-time data calibration. The specific calculation formula is as follows: ； In the formula: The characteristic thickness for heat transfer is specifically the vertical distance from the pipe wall to the floor surface; The overall thermal diffusivity of the heat storage structure is... Specifically ,in The thermal conductivity is preset. Where c is the density and c is the specific heat capacity of the material; These are the preset dynamic calibration coefficients; The actual time difference between the time of instruction issuance and the change in the preset temperature gradient of the surface temperature of the heating terminal is periodically extracted from historical operation data. The calculated lag time is iteratively corrected by Kalman filtering algorithm, and the dynamic calibration coefficient is updated in real time to obtain accurate lag time.

7. The intelligent building heating management system based on indoor environmental parameter adjustment according to claim 1, characterized in that, Establish indoor comprehensive heat capacity And calculate the static heat demand based on the overall indoor heat capacity. The specific calculation formula is as follows: ； ； In the formula: The upper limit threshold is generated in real time by the dynamic comfort assessment unit; This refers to the real-time indoor air temperature. The indoor total heat capacity; , , These are the density, specific heat capacity, and volume of indoor air, respectively. , The mass and specific heat capacity of each equivalent indoor heat-absorbing component. Obtain the indoor cooling load loss rate and the hysteresis time of heat transfer Calculate dynamic cooling loss heat The specific calculation formula is as follows: ； in: In order to delay time The average indoor cooling load loss rate is dynamically affected by the indoor-outdoor temperature difference. The lag time for residual heat to transfer from the heat source to the indoor surface; static heat demand With dynamic cooling loss heat By performing superposition and synthesis, the final theoretical heat compensation is obtained. The specific calculation formula is as follows: ； When the dynamic residual heat is greater than the theoretical compensation heat, it is determined that the heat accumulated at the bottom layer has met the heating demand, and a command to reduce the opening degree of the heating valve is output.

8. The intelligent building heating management system based on indoor environmental parameter adjustment according to claim 1, characterized in that, The specific process for predicting the remaining time for the room temperature to drop to the lower threshold value based on the indoor cooling load loss rate is as follows: Real-time acquisition of current indoor air temperature Outdoor ambient temperature and the lower limit threshold value output by the dynamic comfort assessment unit. And obtain the total heat transfer coefficient of the current building space. The total heat transfer coefficient includes heat conduction loss from the building envelope (walls, doors and windows) and heat loss from infiltration ventilation; Calculate the indoor cooling load loss rate at current time t based on Newton's law of cooling. The specific calculation formula is as follows: ； in, This refers to the real-time indoor air temperature. The total heat storage capacity of indoor air and the shallow building envelope involved in heat exchange is obtained, and the total heat storage capacity is defined as the building's equivalent heat capacity. It satisfies the following energy conservation differential equation: ； Will We perform the integration, using the current time t=0 as the initial condition. = Solve for the threshold value when the temperature drops to the lower limit. Remaining time required The integration process is as follows: ； Calculate the remaining time : 。

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