Hospital public building temperature fine energy-saving control method

Abstract

The application discloses a hospital public building temperature fine energy-saving regulation method, comprising the following steps: based on the use demand and energy use purpose of different wards, a fine hierarchical management framework is established; for a regular inpatient ward, the opening and closing state data of doors and windows and the ward thermal state data are collected, a window magnet control air conditioner operation model is used, and a multi-step prediction control method is adopted to execute control air conditioner operation and adjust the opening and closing state of doors and windows; for different types of special wards, according to the required thermal parameters of the current type of ward, under the MPC model prediction control framework and by using the multi-step prediction control method, the corresponding thermal equipment is regulated and optimized; for an outpatient ward, according to the people flow density, temperature and indoor and outdoor temperature difference, based on a cold load prediction model and by using the multi-step prediction control method, the output operation of the outpatient air conditioner is optimized; the application realizes the monitoring and accurate prediction of the thermal conditions in the ward based on the model prediction control (MPC) method and the window magnet switch.

Description

Technical Field

[0001] This invention relates to the field of intelligent building energy-saving technology, and more specifically to a method for refined energy-saving temperature control in public buildings such as hospitals. Background Technology

[0002] As a special type of public institution, hospitals undertake functions such as medical services, public health, emergency rescue, and scientific research. With the improvement of people's living standards and the continuous improvement of medical and health service capabilities, the energy consumption of hospital buildings is constantly increasing. Air conditioning and heating, as a major energy consumption item in hospital buildings, accounts for 40%-50% of the total energy consumption of the building. The air conditioning system of hospital buildings has its own special characteristics. On the one hand, it has common characteristics such as dense population, large resource consumption, and significant social impact. On the other hand, it also has specific characteristics such as complex system functions, environmental sensitivity, and high safety requirements. Therefore, developing specific green and low-carbon air conditioning control technologies for hospital application scenarios is an inevitable trend in the development of in-depth energy conservation.

[0003] In hospital-type public institutions, the management and control requirements for indoor thermal parameters vary depending on the function of each room. For example, negative pressure wards are among the most energy-intensive areas for air conditioning systems. At the same time, negative pressure wards and the isolation areas they are located in are crucial locations for treating infectious disease patients, making it difficult to simultaneously achieve safety and energy efficiency goals. To prevent cross-infection, redundant negative pressure control is currently employed, but excessively high exhaust pressure can lead to excessively low indoor temperatures. Furthermore, current pressure differential control in negative pressure isolation wards primarily relies on static control with doors and windows closed. However, when the doors are opened and people enter or exit, the negative pressure differential immediately disappears and remains at a static pressure. In this state, it is less effective in preventing airflow and pollutants from escaping through the doorway. Therefore, it is necessary to dynamically adjust the supply and exhaust air volumes to maintain the negative pressure gradient and ensure directional airflow distribution, thereby minimizing the leakage of viral aerosols.

[0004] In addition, the air conditioning systems in hospital-type public buildings have high requirements for environmental comfort. Due to the special needs of patients, it is necessary to ensure ventilation and appropriate temperature and humidity control. For example, in general wards and outpatient clinics, the temperature is controlled at 18-26℃ and the relative humidity is 40-60%. At the same time, outpatient clinics and general wards have high traffic and long air conditioning operation time. In order to meet the requirements of air freshness and comfort, as well as some personal behaviors of users, such as opening windows and doors for ventilation during air conditioning use, a large amount of air conditioning cooling capacity is wasted. At the same time, condensation at the air vents is likely to cause problems such as slippery floors and mold on the ceiling.

[0005] Currently, the most common method to address this issue is to adjust the cooling system based on the temperature difference between the set temperature and the actual detected temperature. This requires residents to manually open and close doors and windows, which can lead to significant energy waste if no one is home.

[0006] With the popularization of smart building technology, some new technologies are gradually being introduced into air conditioning management systems. For example, window and door sensors are connected to the energy management system. Temperature and humidity sensors detect the indoor thermal state and the opening and closing of doors and windows, input feedback signals into the energy management system, and output execution signals to control the operation of the air conditioning system, thereby realizing the intelligent management and control of the building.

[0007] However, most current systems have simplified the management mode in terms of the linkage control of door and window opening and closing status and air conditioning management. Most of them only consider economic needs and do not consider the user's needs for temperature and humidity and the stability of indoor thermal conditions.

[0008] Therefore, how to provide a refined energy-saving control method for indoor air based on the special operating characteristics of public institutions such as hospitals, so as to achieve energy saving and carbon reduction while meeting safety, user comfort, and negative pressure requirements, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0009] In view of this, the present invention provides a method for refined energy-saving temperature control in hospital-type public buildings to solve some of the technical problems mentioned in the background art.

[0010] To achieve the above objectives, the present invention adopts the following technical solution:

[0011] A method for refined energy-saving temperature control in hospital-type public buildings includes the following steps:

[0012] S1. Based on the different usage needs and energy purposes of different wards, establish a refined hierarchical management structure, including regular inpatient wards, special wards and outpatient wards. Special wards include Class I, Class II and Class III special wards.

[0013] S2. Establish a window magnetic control air conditioning operation model for regular inpatient wards, and based on the MPC model predictive control, establish temperature and humidity predictive control models, temperature and humidity-negative pressure joint predictive control models, and temperature-negative pressure joint predictive models for different types of special wards, as well as a cooling load prediction model for outpatient wards.

[0014] S3. For regular inpatient wards, collect data on the opening and closing status of doors and windows and the thermal status of the wards. Based on the window magnetic control air conditioning operation model and using a multi-step predictive control method, execute the control of air conditioning operation and adjust the opening and closing status of doors and windows.

[0015] S4. For different types of special wards, based on the thermal parameters that need to be controlled for the current type of ward, the corresponding thermal equipment is regulated and optimized under the MPC model predictive control framework and by adopting the multi-step predictive control method;

[0016] S5. For outpatient wards, optimize the operation of outpatient air conditioning output based on the cooling load prediction model and multi-step predictive control method, according to the population density, temperature and indoor-outdoor temperature difference.

[0017] Preferably, before step S1, the door and window bodies are sequentially connected to the magnetic induction structure, the central temperature control structure, and the air conditioning actuator. The model is designed to install the magnetic induction structure on the aluminum alloy window of the balcony in each room. The magnetic induction structure is normally closed. The switch part of the magnetic induction structure switch is equipped with a lead wire, which is a signal wire. The lead wire is laid to the fan coil controller or control box. The signal line uses a 2-core RS485 wire. When multiple window magnets are connected, a 2-core RS485 wire is selected and connected in a daisy-chain manner. The connected line is connected to the air conditioning thermostat or fan coil control box. The window magnet and the thermostat are connected through a 2-core RS485 wire.

[0018] Preferably, the multi-step predictive control method is as follows: by predicting the state parameters of regular inpatient wards, special wards and outpatient wards after a multi-step period, within each unit step, the minimum deviation is selected from the prediction results as the target, and the multi-objective particle swarm optimization algorithm is used to optimize the combined output of the equipment to obtain the optimal control result of the ward equipment.

[0019] Preferably, the specific content of step S3 is as follows:

[0020] The window magnetic sensor is used to detect the opening and closing of doors and windows in real time, and the temperature control sensor is used to measure the temperature in real time, so as to calculate the actual deviation between the actual indoor temperature and the preset indoor temperature.

[0021] If the deviation is significant, a response alarm will be triggered. If there is no response after the buffer period, the air conditioner will be forcibly shut down by controlling the operation of the fan coil unit.

[0022] If the deviation is small, the opening and closing degree of the doors and windows is predicted and controlled by the window magnetic control air conditioning operation model based on the temperature deviation and the frequency disturbance of door and window opening and closing. In each step of the prediction optimization, the positive or negative status of the current temperature difference is determined, and the opening and closing degree of the doors and windows is predicted and adjusted under different deviation conditions. The prediction process is repeated in multiple steps to obtain all control results of the window magnetic opening. The optimal control result is obtained by using the particle swarm optimization algorithm to find the best control result under all prediction results. The opening and closing degree of the doors and windows in the first step corresponding to the optimal control result is selected as the actual door and window control.

[0023] Preferably, the specific content of step S4 is as follows:

[0024] S41. Detect indoor and outdoor status parameters of special wards through differential pressure sensors, temperature sensors and humidity sensors, including differential pressure in different areas of special wards, indoor temperature, and exhaust air moisture content and humidity;

[0025] S42. Determine the type of special ward, using the deviation of the corresponding thermal parameters as input, and combining the outdoor environmental conditions, flow parameters and magnetic signals as disturbance factors. Then, use the temperature and humidity prediction and control model, the temperature and humidity-negative pressure joint prediction and control model and the temperature-negative pressure joint prediction model corresponding to the first, second and third types of special wards respectively to predict the output of the relevant equipment. Each time, the state parameters for the next three steps are predicted.

[0026] S43. Add the thermal parameters that need to be controlled in the current type of special ward into the objective function by coefficients;

[0027] S44. Under multi-step prediction, the particle swarm optimization algorithm is compared with the objective function under multiple conditions to find the minimum error as the output result. The first prediction step of each optimal prediction result is used as the input signal, so as to obtain the optimization results of relevant processing equipment that meet the needs of various types of special wards.

[0028] Preferably, in step S42, the deviation of the corresponding thermodynamic parameters is as follows: within each step, the indoor water vapor pressure, humidity and enthalpy value under different states of the window magnetic switch are estimated based on the thermal state equation, and the deviation of the current parameters from the preset parameters is monitored in the temperature and humidity sensor and the negative pressure sensor.

[0029] Preferably, in step S42, the output prediction of the corresponding related equipment in the temperature and humidity prediction control model is the output of the air conditioning water-cooled unit, the output prediction of the corresponding related equipment in the temperature and humidity-negative pressure joint prediction control model is the output of the air conditioning water-cooled unit, the air supply unit and the exhaust unit, and the output prediction of the corresponding related equipment in the temperature-negative pressure joint prediction model is the output of the air conditioner, the air supply unit and the exhaust unit.

[0030] Preferably, each unit step prediction result has three control variables: the compressor controlling the air conditioning water-cooled unit, the air supply velocity, and the valve openings for both air supply and exhaust. For n steps, there are 3... n One prediction result.

[0031] Preferably, the temperature and humidity control uses the real-time and expected temperature and humidity deviations as control inputs, compares the deviations with preset limits, and provides feedback to modify the operation of the chiller and air conditioner.

[0032] In the coupled model predictive control of temperature, humidity and negative pressure load, the negative pressure control of the ward is achieved by a differential pressure sensor monitoring the differential pressure state in the ward in real time and feeding the monitoring data back to the central processing unit. The central processing unit generates execution commands, and after receiving the control signals, the command receiver adjusts the opening and closing of the system's exhaust fan unit to control the changes in the air supply parameters in the ward, thereby controlling the negative pressure level in the ward. The differential pressure control is achieved by setting expected limits and comparing them. Based on the deviation between the real-time differential pressure and the differential pressure of each limit, the signal is converted into the opening and closing degree of the air volume regulating valve, thereby changing the opening degree of the exhaust regulating valve.

[0033] Preferably, the specific content of step S5 is as follows:

[0034] S51. The current population density of the outpatient department is monitored by sensors as the disturbance input, and the outdoor temperature and the indoor temperature of the outpatient ward are used as known state parameters in the prediction model. The preset temperature of the air conditioner is obtained by setting the temperature comfort range according to the season.

[0035] S52. The actual air conditioning output required to achieve the target temperature is obtained by calculating using a cooling load prediction model;

[0036] S53. Based on the prediction model, predict the room temperature change after multiple steps, and use the minimum deviation between the predicted room temperature and the expected room temperature as the objective function. Under the particle swarm optimization algorithm, the optimal air conditioning output is obtained through optimization. The air conditioning output of the first step is used as the actual air conditioning output of the next period. Update the temperature in the ward, enter the next step, and repeat the process to complete the predictive control during the entire optimization period.

[0037] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method for refined energy-saving temperature control in hospital-type public buildings. Based on the temperature-humidity synergy and temperature-negative pressure linkage characteristics of temperature regulation in hospital-type public institutions, and the current situation where the coupling control of multiple air index parameters is relatively crude, this invention aims at safety, comfort, and system energy saving. Based on the Model Predictive Control (MPC) method combined with window magnetic switches, it realizes the monitoring and accurate prediction of the thermal conditions in the ward, and effectively realizes the automation and refinement of the overall system operation. It can effectively achieve stable operation of temperature and humidity. Compared with traditional monitoring and manual control, the control of the present invention has real-time and predictive capabilities, takes into account the control optimization under multiple objectives, and combines intelligent control technology to achieve efficient control and management of hospital wards. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0039] Figure 1 This is a schematic diagram of a method for refined energy-saving temperature control in hospital-type public buildings provided by the present invention;

[0040] Figure 2 This invention provides the intended process for controlling the air conditioning in a conventional inpatient ward.

[0041] Figure 3 This invention provides a schematic diagram of the special ward status management process.

[0042] Figure 4 This is a schematic diagram of the cooling load prediction process for outpatient rooms provided by the present invention;

[0043] Figure 5 This is a schematic diagram of the multi-step predictive control optimization algorithm based on the MPC framework provided by the present invention. Detailed Implementation

[0044] 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.

[0045] This invention discloses a method for refined energy-saving temperature control in hospital-type public buildings, such as... Figure 1 As shown, it includes the following steps:

[0046] S1. Based on the different usage needs and energy purposes of different wards, establish a refined hierarchical management structure, including regular inpatient wards, special wards and outpatient wards. Special wards include Class I, Class II and Class III special wards.

[0047] S2. Establish a window magnetic control air conditioning operation model for regular inpatient wards, and based on the MPC model predictive control, establish temperature and humidity predictive control models, temperature and humidity-negative pressure joint predictive control models, and temperature-negative pressure joint predictive models for different types of special wards, as well as a cooling load prediction model for outpatient wards.

[0048] S3. For regular inpatient wards, collect data on the opening and closing status of doors and windows and the thermal status of the wards. Based on the window magnetic control air conditioning operation model and using a multi-step predictive control method, execute the control of air conditioning operation and adjust the opening and closing status of doors and windows.

[0049] S4. For different types of special wards, based on the thermal parameters that need to be controlled for the current type of ward, the corresponding thermal equipment is regulated and optimized under the MPC model predictive control framework and by adopting the multi-step predictive control method;

[0050] S5. For outpatient wards, optimize the operation of outpatient air conditioning output based on the cooling load prediction model and multi-step predictive control method, according to the population density, temperature and indoor-outdoor temperature difference.

[0051] To further implement the above technical solution, before step S1, the door and window bodies are sequentially connected to the magnetic induction structure, the central temperature control structure, and the air conditioning actuator. The model proposes to install the magnetic induction structure on the aluminum alloy window of the balcony in each room. The magnetic induction structure is normally closed. The switch part of the magnetic induction structure switch is equipped with a lead wire, which is a signal line. The lead wire is laid to the fan coil controller or control box. The signal line uses a 2-core RS485 line. When multiple window magnets are connected, a 2-core RS485 line is selected and connected in series. The series line is connected to the air conditioning thermostat or fan coil control box. The window magnet and the thermostat are connected through a 2-core RS485 line.

[0052] To further implement the above technical solution, the specific content of the multi-step predictive control method is as follows: by predicting the state parameters of regular inpatient wards, special wards and outpatient wards after a multi-step period, within each unit step, the minimum deviation is selected as the target from the prediction results, and the multi-objective particle swarm optimization algorithm is used to optimize the combined output of the equipment to obtain the optimal control result of the ward equipment.

[0053] To further implement the above technical solutions, such as Figure 2 As shown, the specific content of step S3 is as follows:

[0054] The window magnetic sensor is used to detect the opening and closing of doors and windows in real time, and the temperature control sensor is used to measure the temperature in real time, so as to calculate the actual deviation between the actual indoor temperature and the preset indoor temperature.

[0055] Specifically: Connect the window magnetic sensor to the managed doors and windows, use the window magnetic sensor to detect the opening and closing of the doors and windows in real time, transmit the data to the energy management system, and output the feedback signal to the magnetic sensor receiver. The magnetic sensor receiver sends a signal to the temperature control sensor installed indoors, triggering the temperature control sensor to measure the temperature in real time and calculate the actual deviation between the actual indoor temperature and the preset indoor temperature.

[0056] If the deviation is large, it is judged that the user may have forgotten to close the doors and windows for a long time. Prolonged opening of doors and windows will cause a lot of air conditioning cooling capacity to be wasted. In order to avoid this situation, the temperature and humidity control detector will feed back the deviation to the window magnetic sensor in real time, triggering a response alarm. If there is no response after the buffer time, the air conditioner will be forcibly shut down by controlling the operation of the fan coil unit.

[0057] If the deviation is small, it indicates that the opening and closing time of the doors and windows is short or the opening and closing range is small. This may be due to patients entering and exiting or short-term ventilation. In this case, the model can predict the optimal ward thermal conditions and control the opening and closing of doors and windows through the output signal of the window magnetic switch. Specifically: based on the temperature deviation and the disturbance of the door and window opening and closing frequency, the window magnetic switch control air conditioning operation model predicts and controls the opening and closing degree of doors and windows. In each step of the prediction optimization, the positive or negative status of the current temperature difference is judged, and the control adjustment of the opening and closing degree of doors and windows under different deviation conditions is predicted respectively. The prediction process is repeated in multiple steps to obtain all control results of the window magnetic switch opening degree. The window magnetic switch only has two options: increase and decrease. Theoretically, after n steps, the prediction results have 2 n The optimal control result is obtained by using the particle swarm optimization algorithm to find the best control result under all prediction results. The opening and closing state of the doors and windows corresponding to the first step size under the optimal control result is selected as the actual door and window control.

[0058] To further implement the above technical solutions, such as Figure 3 As shown, the specific content of step S4 is as follows:

[0059] S41. Detect indoor and outdoor status parameters of special wards through differential pressure sensors, temperature sensors and humidity sensors, including differential pressure in different areas of special wards, indoor temperature, and exhaust air moisture content and humidity;

[0060] S42. Determine the type of special ward, using the deviation of the corresponding thermal parameters as input, and combining the outdoor environmental conditions, flow parameters and magnetic signals as disturbance factors. Then, use the temperature and humidity prediction and control model, the temperature and humidity-negative pressure joint prediction and control model and the temperature-negative pressure joint prediction model corresponding to the first, second and third types of special wards respectively to predict the output of the relevant equipment. Each time, the state parameters for the next three steps are predicted.

[0061] S43. Add the thermal parameters that need to be controlled in the current type of special ward into the objective function by coefficients;

[0062] S44. Under multi-step prediction, the particle swarm optimization algorithm is compared with the objective function under multiple conditions to find the minimum error as the output result. The first prediction step of each optimal prediction result is used as the input signal, so as to obtain the optimization results of relevant processing equipment that meet the needs of various types of special wards.

[0063] To further implement the above technical solution, in step S42, the deviation of the corresponding thermodynamic parameters is as follows: within each step, the indoor water vapor pressure, humidity and enthalpy value under different states of the window magnetic switch are estimated based on the thermal state equation, and the deviation of the current parameters from the preset parameters is monitored in the temperature and humidity sensor and the negative pressure sensor.

[0064] To further implement the above technical solution, in step S42, the output prediction of the corresponding related equipment in the temperature and humidity prediction control model is the output of the air conditioning water-cooled unit, the output prediction of the corresponding related equipment in the temperature and humidity-negative pressure joint prediction control model is the output of the air conditioning water-cooled unit, the air supply unit and the exhaust unit, and the output prediction of the corresponding related equipment in the temperature-negative pressure joint prediction model is the output of the air conditioner, the air supply unit and the exhaust unit.

[0065] To further implement the above technical solution, each unit step prediction result has three control variables: the compressor controlling the air conditioning water-cooled unit, the air supply velocity, and the valve openings for air supply and exhaust. For n steps, there are 3... n One prediction result.

[0066] To further implement the above technical solution, the temperature and humidity control uses the real-time and expected temperature and humidity deviations as control inputs, compares the deviations with preset limits, and provides feedback to modify the operation of the chiller and air conditioner.

[0067] For stable temperature and humidity control, the controller is a PLC controller, which is connected to temperature sensors, humidity sensors, on / off status sensors, and command receivers; for real-time display of indoor thermal status settings, it realizes visualization of the thermal parameters in the ward, monitors the operating status information, fault information, and alarm signals in the ward; it is also connected to historical data storage devices;

[0068] In the coupled model predictive control of temperature, humidity and negative pressure load, the negative pressure control of the ward is achieved by a differential pressure sensor monitoring the differential pressure in the ward in real time and feeding the monitoring data back to the central processing unit. The central processing unit generates execution commands, and after receiving the control signals, the command receiver adjusts the opening and closing of the exhaust fan unit of the system to control the changes in the air supply parameters in the ward, thereby controlling the negative pressure level in the ward. The differential pressure control is achieved by setting expected limits and comparing them. Based on the deviation between the real-time differential pressure and the differential pressure of each limit, the signal is converted into the opening and closing degree of the air volume regulating valve, thereby changing the opening degree of the exhaust regulating valve.

[0069] The negative pressure controller is a PLC controller, connected to a differential pressure sensor, an open / closed status sensor, and a command receiver; it is also connected to a display screen to display the real-time operating status information, fault information, and alarm signals of the negative pressure isolation ward differential pressure control system; it is also connected to a historical data storage device.

[0070] In this embodiment, for the differential pressure control in the negative pressure ward, different deviation ranges are set, and the valve opening is adjusted according to the current deviation range. Different deviation ranges are set for high and low differential pressure. For small deviations, an alarm signal is issued. For large deviations, the opening and closing of the exhaust valve is controlled according to the current required differential pressure.

[0071] Temperature and humidity control in wards is achieved using water-cooled chiller units in air conditioning systems. Temperature control involves a condenser, compressor, evaporator, and expansion valve, which work together to form a complete refrigeration cycle. In optimization, temperature deviation is used as the input for predictive control to adjust the compressor power. Humidity control is also achieved using chiller units, but the process differs from refrigeration in air handling units. Generally, in the air handling section, the fan speed needs to be changed to alter the air temperature by varying the contact time between the air and the copper pipes, thereby affecting the humidity.

[0072] Specifically:

[0073] The output of the air conditioning water-cooled unit is adjusted according to the following formulas to control temperature and humidity:

[0074] Calculate the saturated water vapor pressure:

[0075]

[0076] Where t is the temperature;

[0077] Calculate moisture content:

[0078]

[0079] Among them, P sb Where P is the saturated water vapor pressure, and P is the atmospheric pressure with a value of 101325. Relative humidity;

[0080] Calculate the enthalpy of air:

[0081] h = (1.006 + 1.86 × d) × t + 2501 × d Air leakage in negative pressure ward:

[0082]

[0083] Among them, Q 漏 The value represents the air leakage rate, P represents the pressure difference, and A represents the area of ​​the door, window, or gap.

[0084] The air leakage rate is approximately equal to the number of air changes.

[0085]

[0086] After taking into account the disturbance caused by frequent door openings, the exhaust air temperature of the air conditioning unit is predicted using the following formula based on the current thermal parameters in the ward:

[0087]

[0088] Where N is the number of air exchanges, t 排i+Δt The next time period's air conditioning exhaust temperature, t 标i t represents the target room temperature at the current moment. 内i Let t be the current indoor temperature. 外i Let M be the outdoor temperature at the current moment, Δt be the time step, and M be the outdoor temperature. i This shows the current ward occupancy status. The heat dissipation coefficient of patients in the ward;

[0089] The relationship between air conditioner output power and air conditioner exhaust temperature:

[0090]

[0091] Among them, Q 冷 For air conditioning cooling load, Q 热 For air conditioning heat load, T 内 Indoor temperature, T 排 This refers to the exhaust air temperature.

[0092] The air conditioning cooling and heating load is:

[0093] Q 冷 =q m ×(h 外 -h 露 )

[0094] Q 热 =q m ×(h 排 -h 露 )

[0095] Where, q m For exhaust mass flow rate, h 外 The outdoor air enthalpy value, h 露 h is the enthalpy value at the dew point. 排 The enthalpy value of the air exhausted from the air conditioner;

[0096] The temperature and humidity in the wards are regulated through a fresh air conditioning system. The energy consumption calculation formula for the air conditioning chiller unit is the same as that for the wards. It is only necessary to flexibly control the number of air exchanges to avoid energy loss.

[0097] The humidity prediction model is determined by the following formula:

[0098]

[0099] Where, ΔH i+ΔtThe room humidity for the next time period, ΔS i H represents the heat absorbed by the room within a time step, k is the room's heat transfer coefficient, and H is the heat absorbed by the room within a time step. i H0 is the current room humidity, v is the air conditioner exhaust humidity, Δt is the time step, and a and b are model parameters that are continuously refined through iteration.

[0100] The relationship between air conditioner fan power and air conditioner exhaust humidity is as follows:

[0101]

[0102] Among them, P a Where T is standard atmospheric pressure, R is the ideal gas constant, and T is the pressure of the gas. 排 E is the exhaust temperature, Q is the water vapor partial pressure, ΔP is the pressure difference generated by the fan, and η1 is the fan efficiency.

[0103] After predicting and calculating the equipment output, optimization results are obtained using an optimization algorithm within the MPC framework, such as... Figure 4 As shown, during the calculation, the deviation between the actual and predicted values ​​of each step is used as input into the constructed model for prediction. In each control period, a multi-objective particle swarm optimization algorithm is introduced to seek the optimal solution set based on the objective function constructed by the additive coefficient. The output results at time k+1 in the future period N are recorded in the system. This process is repeated to achieve rolling optimization of refined management of hospital equipment.

[0104] For the control situations described above, the output prediction is performed under the MPC framework, and the minimum deviation under multiple results is optimized by the particle swarm optimization algorithm. The output of the first step corresponding to the optimal result is taken as the actual output.

[0105] To further implement the above technical solutions, such as Figure 5 As shown, the specific content of step S5 is as follows:

[0106] S51. The current population density of the outpatient department is monitored by sensors as the disturbance input, and the outdoor temperature and the indoor temperature of the outpatient ward are used as known state parameters in the prediction model. The preset temperature of the air conditioner is obtained by setting the temperature comfort range according to the season.

[0107] S52. The actual air conditioning output required to achieve the target temperature is obtained by calculating using a cooling load prediction model;

[0108] Cooling load prediction model:

[0109]

[0110] Among them, Pac i+Δt To predict the power output of air conditioning for the next time period, n peo，iLet T be the current patient density in the outpatient department, ω be the human body heat dissipation coefficient per unit time, and T be the patient density in the outpatient department. out，i T represents the current outdoor temperature. in，i The current indoor temperature, T pre，i ρ is the expected steady-state temperature at the current moment. air c is the density of the air inside the outpatient department. air V is the specific heat capacity of the air inside the outpatient department. r β represents the room volume of the outpatient department, and β represents the air conditioning energy efficiency ratio.

[0111] S53. Based on the prediction model, predict the room temperature change after multiple steps, and use the minimum deviation between the predicted room temperature and the expected room temperature as the objective function. Under the particle swarm optimization algorithm, the optimal air conditioning output is obtained through optimization. The air conditioning output of the first step is used as the actual air conditioning output of the next period. Update the temperature in the ward, enter the next step, and repeat the process to complete the predictive control during the entire optimization period.

[0112] A computer-readable storage medium having a computer program stored thereon, characterized in that, when executed by a processor, the computer program implements a method for refined energy-saving temperature control in hospital-type public buildings.

[0113] A processing terminal includes a memory and a processor. The memory stores a computer program that can run on the processor. The processor executes the computer program to implement a method for refined energy-saving temperature control in hospital-type public buildings.

[0114] This invention provides a method for refined energy-saving temperature control in hospital-type public buildings. Through an IoT system integrating doors, windows, sensors, and air conditioning terminal actuators, it enables simultaneous monitoring of thermal parameters and door / window opening / closing status within patient rooms. The monitored data is fed back to the energy management system in real-time for calculation. Within the system, a model predictive control framework predicts the operating status of window magnetic switches. Simultaneously, the patient room data is transmitted in real-time via a data transmission network to a pre-set task processing module in the central temperature control structure for processing, thereby executing operations on the air conditioning terminal's operating status. In actual operation, real-time deviations in the patient room's thermal condition are synchronously uploaded to the central management system for visualized management. The execution terminal judges the deviation and outputs an execution signal to the window magnetic sensor. Upon receiving the signal, the window magnetic sensor triggers a warning, and after a buffer period, the air conditioning is switched on or off. By integrating multiple IoT sensors and window magnetic devices, the temperature and humidity in the ward are precisely adjusted. At the same time, window magnetic control avoids the situation where windows and doors are kept open for a long time when the air conditioner is running, thus saving energy and solving the problem of condensation. The integration of multiple IoT devices with sensors increases the intelligent means of system operation, effectively realizing the automation and precision of the overall system operation. The entire system can effectively achieve stable operation of temperature and humidity.

[0115] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0116] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for refined energy-saving temperature control in hospital-type public buildings, characterized in that, Includes the following steps: S1. Based on the different usage needs and energy purposes of different wards, establish a refined hierarchical management structure, including regular inpatient wards, special wards and outpatient wards. Special wards include Class I, Class II and Class III special wards. S2. Establish a window magnetic control air conditioning operation model for regular inpatient wards, and based on the MPC model predictive control, establish temperature and humidity predictive control models, temperature and humidity-negative pressure joint predictive control models, and temperature-negative pressure joint predictive models for different types of special wards, as well as a cooling load prediction model for outpatient wards. S3. For regular inpatient wards, collect data on the opening and closing status of doors and windows and the thermal status of the wards. Based on the window magnetic control air conditioning operation model and using a multi-step predictive control method, execute the control of air conditioning operation and adjust the opening and closing status of doors and windows. S4. For different types of special wards, based on the thermal parameters that need to be controlled for the current type of ward, the corresponding thermal equipment is regulated and optimized under the MPC model predictive control framework and by adopting the multi-step predictive control method; S5. For outpatient wards, optimize the operation of outpatient air conditioning output based on the cooling load prediction model and multi-step predictive control method, according to the population density, temperature and indoor-outdoor temperature difference. The specific content of the multi-step predictive control method is as follows: by predicting the state parameters of regular inpatient wards, special wards and outpatient wards after a multi-step period, within each unit step, the minimum deviation is selected from the prediction results as the target, and the multi-objective particle swarm algorithm is used to optimize the combined output of the equipment to obtain the optimal control result of the ward equipment. The specific content of step S4 is as follows: S41. Detect indoor and outdoor status parameters of special wards through differential pressure sensors, temperature sensors and humidity sensors, including differential pressure in different areas of special wards, indoor temperature, and exhaust air moisture content and humidity; S42. Determine the type of special ward, using the deviation of the corresponding thermal parameters as input, and combining the outdoor environmental conditions, flow parameters and magnetic signals as disturbance factors. Then, use the temperature and humidity prediction and control model, the temperature and humidity-negative pressure joint prediction and control model and the temperature-negative pressure joint prediction model corresponding to the first, second and third types of special wards respectively to predict the output of the relevant equipment. Each time, the state parameters for the next three steps are predicted. S43. Add the thermal parameters that need to be controlled in the current type of special ward into the objective function by coefficients; S44. Under multi-step prediction, the particle swarm optimization algorithm is compared with the objective function under multiple conditions to find the minimum error as the output result. The first prediction step of each optimal prediction result is used as the input signal, so as to obtain the optimization results of relevant processing equipment that meet the needs of various types of special wards.

2. The method for refined energy-saving temperature control in hospital-type public buildings according to claim 1, characterized in that, Before step S1, the door and window bodies are sequentially connected to the magnetic induction structure, the central temperature control structure, and the air conditioning actuator. The model plans to install the magnetic induction structure on the aluminum alloy window of the balcony in each room. The magnetic induction structure is normally closed. The switch part of the magnetic induction structure switch is equipped with a lead wire, which is a signal line. The lead wire is laid to the fan coil controller or control box. The signal line uses a 2-core RS485 line. When multiple window magnets are connected, a 2-core RS485 line is selected and connected in a daisy-chain manner. The connected line is connected to the air conditioning thermostat or fan coil control box. The window magnet and the thermostat are connected through a 2-core RS485 line.

3. The method for refined energy-saving temperature control in hospital-type public buildings according to claim 1, characterized in that, The specific content of step S3 is as follows: The window magnetic sensor is used to detect the opening and closing of doors and windows in real time, and the temperature control sensor is used to measure the temperature in real time, so as to calculate the actual deviation between the actual indoor temperature and the preset indoor temperature. If the deviation is significant, a response alarm will be triggered. If there is no response after the buffer period, the air conditioner will be forcibly shut down by controlling the operation of the fan coil unit. If the deviation is small, the opening and closing degree of the doors and windows is predicted and controlled by the window magnetic control air conditioning operation model based on the temperature deviation and the frequency disturbance of door and window opening and closing. In each step of the prediction optimization, the positive or negative status of the current temperature difference is determined, and the opening and closing degree of the doors and windows is predicted and adjusted under different deviation conditions. The prediction process is repeated in multiple steps to obtain all control results of the window magnetic opening. The optimal control result is obtained by using the particle swarm optimization algorithm to find the best control result under all prediction results. The opening and closing degree of the doors and windows in the first step corresponding to the optimal control result is selected as the actual door and window control.

4. The method for refined energy-saving temperature control in hospital-type public buildings according to claim 1, characterized in that, In step S42, the deviation of the corresponding thermodynamic parameters is as follows: within each step, the indoor water vapor pressure, humidity and enthalpy under different states of the window magnetic switch are estimated based on the thermal state equation, and the deviation of the current parameters from the preset parameters is monitored in the temperature and humidity sensor and the negative pressure sensor.

5. A method for refined energy-saving temperature control in hospital-type public buildings according to claim 1, characterized in that, In step S42, the output prediction of the corresponding equipment in the temperature and humidity prediction control model is the output of the air conditioning water-cooled unit; the output prediction of the corresponding equipment in the temperature and humidity-negative pressure joint prediction control model is the output of the air conditioning water-cooled unit, the air supply unit, and the exhaust unit; and the output prediction of the corresponding equipment in the temperature-negative pressure joint prediction model is the output of the air conditioning unit, the air supply unit, and the exhaust unit.

6. The method for refined energy-saving temperature control in hospital-type public buildings according to claim 1, characterized in that, Each unit step prediction result has three control variables: the compressor controlling the air conditioning water-cooled unit, the air supply velocity, and the valve openings for both supply and exhaust air. There are 3 control variables for each n-step prediction. n One prediction result.

7. A method for refined energy-saving temperature control in hospital-type public buildings according to claim 1, characterized in that, Temperature and humidity control uses real-time and expected temperature and humidity deviations as control inputs, compares the deviations with preset limits, and provides feedback to modify the operation of the chiller and air conditioner. In the coupled model predictive control of temperature, humidity and negative pressure load, the negative pressure control of the ward is achieved by a differential pressure sensor monitoring the differential pressure state in the ward in real time and feeding the monitoring data back to the central processing unit. The central processing unit generates execution commands, and after receiving the control signals, the command receiver adjusts the opening and closing of the system's exhaust fan unit to control the changes in the air supply parameters in the ward, thereby controlling the negative pressure level in the ward. The differential pressure control is achieved by setting expected limits and comparing them. Based on the deviation between the real-time differential pressure and the differential pressure of each limit, the signal is converted into the opening and closing degree of the air volume regulating valve, thereby changing the opening degree of the exhaust regulating valve.

8. A method for refined energy-saving temperature control in hospital-type public buildings according to claim 1, characterized in that, The specific content of step S5 is as follows: S51. The current population density of the outpatient department is monitored by sensors as the disturbance input, and the outdoor temperature and the indoor temperature of the outpatient ward are used as known state parameters in the prediction model. The preset temperature of the air conditioner is obtained by setting the temperature comfort range according to the season. S52. The actual air conditioning output required to achieve the target temperature is obtained by calculating using a cooling load prediction model; S53. Based on the prediction model, predict the room temperature change after multiple steps, and use the minimum deviation between the predicted room temperature and the expected room temperature as the objective function. Under the particle swarm optimization algorithm, the optimal air conditioning output is obtained through optimization. The air conditioning output of the first step is used as the actual air conditioning output of the next period. Update the temperature in the ward, enter the next step, and repeat the process to complete the predictive control during the entire optimization period.

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