Fine energy-saving regulation and control method for temperature of hospital public building

Abstract

The invention discloses a hospital public building temperature refined energy-saving regulation and control method, which comprises the following steps: establishing a refined hierarchical management architecture based on use requirements and energy use purposes of different wards; for a conventional inpatient ward, door and window opening and closing state data and ward heat state data are collected, and air conditioner operation control and door and window opening and closing state adjustment are executed based on a window magnetic control air conditioner operation model and by adopting a multi-step prediction control method; for different types of special wards, regulating and optimizing corresponding thermal equipment by adopting a multi-step prediction control method under an MPC model prediction control framework according to thermal parameters required to be controlled by the current type of ward; for outpatient wards, outpatient air conditioner output operation is optimized by adopting a multi-step prediction control method based on a cooling load prediction model according to the flow density, the temperature and the indoor and outdoor temperature difference; according to the invention, monitoring and accurate prediction of the thermal condition in the ward are realized based on the model prediction control MPC method in combination with the window magnetic switch.

Description

Technical Field

[0001] The present invention relates to the field of intelligent building energy-saving technology, and more specifically to a refined energy-saving control method for temperature of a hospital-type public building. Background Art

[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 in my country, the capacity of medical and health services continues to improve, and the energy consumption of hospital buildings continues to increase. Air conditioning and HVAC, as a major item of energy consumption in hospital buildings, account for 40%-50% of the overall energy consumption of the building. The air conditioning system of hospital buildings has its particularity. On the one hand, it has the general characteristics of dense personnel, high resource consumption, and great social impact. In addition, it also has proprietary characteristics such as complex system functions, environmental sensitivity, and high safety requirements. Therefore, the development of specific green and low-carbon air-conditioning control technology for hospital application scenarios is an inevitable trend in the development of deep energy conservation.

[0003] In the air environment regulation of public institutions such as hospitals, different management and control requirements exist for indoor thermal parameters according to different functions of the rooms. For example, negative pressure wards are one of the areas with the highest energy consumption of air conditioning systems. At the same time, negative pressure wards and their isolation areas are important places for treating infectious patients. It is difficult to achieve safety and energy saving goals at the same time. In order to prevent cross infection, redundant negative pressure regulation is currently used. Excessive air extraction will lead to low indoor temperature control. In addition, the current pressure difference control of negative pressure isolation wards mainly adopts static regulation when doors and windows are closed. When the door of the negative pressure isolation ward is opened and people enter and exit, the negative pressure difference will disappear immediately and maintain static pressure. At this time, it has little effect on preventing airflow and pollutants from escaping from the door. Therefore, it is necessary to dynamically adjust the supply and exhaust air volume to maintain the negative pressure gradient, thereby ensuring directional airflow distribution, so as to minimize the leakage of viral aerosols.

[0004] In addition, the air conditioning system in public buildings such as hospitals has high requirements for environmental comfort. Due to the special nature of patients, it is necessary to ensure ventilation, appropriate temperature and humidity control. For example, in general wards and outpatient clinics, the temperature is controlled at 18-26°C and the relative humidity is 40-60%. At the same time, outpatient clinics, general wards and other areas have a large flow of people and the air conditioning runs for a long time. In order to meet the freshness and comfort of the air, as well as some personal behaviors of users, such as opening windows and doors for ventilation during the use of air conditioning, a large amount of air conditioning cooling is wasted, and it is easy to cause condensation at the air outlet, causing problems such as slippery ground and moldy ceiling.

[0005] At present, most methods to solve this problem still use the temperature difference between the set temperature and the actual detected temperature to adjust the cooling condition. Residents are required to sense and manually open and close doors and windows. If there is no one indoors, it will cause a large waste of energy.

[0006] With the popularization of smart building technologies, some new technologies have been gradually introduced into the air-conditioning management system. For example, window and door magnetic sensors are connected to the energy management system, and the indoor thermal state and the opening and closing conditions of doors and windows are detected through temperature and humidity sensors. The feedback signals are input into the energy management system, and execution signals are output 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 the interlocking control of the opening and closing states of doors and windows and air-conditioning management. Most of them only consider the economic requirements and do not consider the requirements for the stability of the user's temperature and humidity and the indoor thermal conditions.

[0008] Therefore, how to provide a refined energy-saving regulation method for indoor air based on the characteristics of special operation places in public institutions such as hospitals, and achieve energy conservation and carbon reduction while meeting safety, user comfort, and negative pressure requirements is an urgent problem for those skilled in the art to solve. Summary of the Invention

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

[0010] To achieve the above object, the present invention adopts the following technical solutions:

[0011] A refined energy-saving regulation method for the temperature of public buildings of hospital type, comprising the following steps:

[0012] S1. Based on the usage requirements and energy consumption purposes of different wards, a refined hierarchical management framework is established, including general inpatient wards, special wards, and outpatient wards. The special wards include the first type, the second type, and the third type of special wards;

[0013] S2. For general inpatient wards, a window magnetic control air-conditioning operation model is established. Based on the MPC model predictive control, a temperature and humidity predictive control model, a temperature and humidity-negative pressure combined predictive control model, and a temperature-negative pressure combined predictive model for different types of special wards, as well as a cooling load predictive model for outpatient wards are established;

[0014] S3. For general inpatient wards, the opening and closing state data of doors and windows and the ward thermal state data are collected. Based on the window magnetic control air-conditioning operation model and using a multi-step prediction control method, the operation of the air-conditioning is controlled and the opening and closing states of the doors and windows are adjusted;

[0015] S4. For different types of special wards, according to the thermal parameters required to be controlled in the current type of ward, under the framework of the MPC model predictive control and using a multi-step prediction control method, the corresponding thermal equipment is regulated and optimized;

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

[0017] Preferably, before step S1, connect the door and window body to the magnetic induction structure, the central temperature control structure, and the air-conditioning execution end in sequence. It is planned to install the magnetic induction structure on the balcony aluminum alloy window of each room. The magnetic induction structure uses a normally closed type; the switch part of the magnetic induction structure switch is equipped with a lead wire, which is a signal wire. Lay the lead wire to the fan coil controller or control box. The signal line uses a 2-core RS485 wire; when multiple window magnets are connected, select a 2-core RS485 wire and adopt a daisy-chain series connection method. The series-connected line is connected to the air-conditioning thermostat or the fan coil control box; the window magnet and the thermostat are connected through a 2-core RS485 wire.

[0018] Preferably, the specific content of the multi-step prediction control method is: by predicting the state parameters of general inpatient wards, special wards, and outpatient wards after multiple time steps, within each unit time step, select the minimum deviation from the prediction results as the target, and use the multi-objective particle swarm optimization algorithm to optimize the combined output of the equipment to obtain the optimal control results of the ward equipment.

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

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

[0021] If the deviation is large, trigger a response alarm. If there is no response after the buffer time, forcibly turn off the air conditioner by controlling the operation of the fan coil.

[0022] If the deviation is small, based on the temperature deviation and the disturbance of the opening and closing frequency of the doors and windows, predict and control the opening and closing degree of the doors and windows through the window magnetic control air-conditioning operation model. Judge the positive and negative conditions of the current temperature difference in each step of the prediction optimization, and predict the control and adjustment of the opening and closing degree of the doors and windows under different deviation conditions respectively. Repeat the prediction process under multiple time steps to obtain all the control results of the window magnetic opening degree. After optimizing through the particle swarm algorithm under all the prediction results, obtain the optimal control result, and select the opening and closing status of the doors and windows in the first time step corresponding to the optimal control result as the actual door and window control.

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

[0024] S41. Detect the indoor and outdoor state parameters of the special ward through the differential pressure sensor, temperature sensor, and humidity sensor, including the differential pressure in different areas of the special ward, the indoor temperature, and the moisture content and humidity of the exhaust air.

[0025] S42. Determine the type of special ward. Using the deviation of the corresponding thermal parameters as the input, and combining the outdoor environmental status, the pedestrian flow parameter, and the magnetic induction signal as disturbance factors, respectively, through the temperature and humidity prediction and control models corresponding to the first type, the second type, and the third type of special wards, the temperature and humidity - negative pressure joint prediction and control model, and the temperature - negative pressure joint prediction model, predict the output of the corresponding related equipment, and predict the state parameters of the next three steps each time.

[0026] S43. Add the thermal parameters to be controlled in the current type of special ward to the objective function by a coefficient.

[0027] S44. Under multi-step prediction, compare the particle swarm algorithms in multiple cases to optimize the objective function to obtain 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 the relevant processing equipment for each type of special ward.

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

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

[0030] Preferably, each unit step prediction result has three control variables, namely, controlling the compressor of the air-conditioning water-cooled unit, the air supply speed, and the valve openings of the air supply and exhaust, and there are 3 n prediction results for n steps.

[0031] Preferably, the temperature and humidity control takes the real-time and expected temperature and humidity deviations as the control input, compares the deviations with the preset limits, and feeds back to modify the working conditions of the chiller and the air conditioner.

[0032] For the coupled model predictive control of temperature-humidity and negative pressure loads, the negative pressure control in the ward monitors the pressure difference state in the ward in real time by a differential pressure sensor, and feeds back the monitoring situation to the central processor in real time. The central processor generates an execution command. After the instruction receiver receives the control signal, it regulates the opening and closing size of the exhaust fan group of the system, controls the change of the air supply parameters in the ward, and then controls the negative pressure level in the ward. The differential pressure control is compared by setting a preset limit value. According to the deviation between the real-time differential pressure and the limit differential pressure, it is converted into a signal of the opening degree of the air volume regulating valve to change the opening degree of the exhaust regulating valve.

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

[0034] S51. The current crowd density in the outpatient department is monitored by a sensor and input as a disturbance quantity. The outdoor temperature condition and the indoor temperature in the outpatient ward are used as known state parameters in the prediction model. The preset temperature of the air conditioner is obtained according to the temperature comfort interval set by the season.

[0035] S52. Calculate through the cooling load prediction model to obtain the actual air conditioner output required to achieve the target temperature.

[0036] S53. According to the prediction model, predict the room temperature change after multiple time steps, and use the minimum value of the deviation between the predicted room temperature and the expected room temperature as the objective function. Under the particle swarm algorithm, obtain the optimal air conditioner output result through optimization. Take the air conditioner output situation in the first time step as the actual air conditioner output in the next time period, update the temperature situation in the ward, enter the next time step, repeat the process, and complete the predictive control within the entire optimization time period.

[0037] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method for refined energy-saving regulation of temperature in hospital public buildings. Based on the characteristics of temperature-humidity coordination and temperature-negative pressure linkage in the temperature regulation of hospital public institutions, and the current situation of relatively extensive coupled control of multiple air index parameters, aiming at safety, comfort and system energy conservation, based on the model predictive control MPC method combined with window magnetic switches, the monitoring and accurate prediction of the thermal conditions in the ward are realized, and the automation and refinement of the overall system operation are effectively achieved. It can effectively realize the stable operation of temperature and humidity. Compared with the traditional monitoring and manual control, the control of the present invention has real-time and predictive properties, takes into account the control optimization under multiple objectives, combines intelligent control technology, and realizes the efficient control and management of hospital wards. Description of the Drawings

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on the provided drawings.

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

[0040] Figure 2 Schematic diagram of the air-conditioning control process in a conventional inpatient ward provided by the present invention;

[0041] Figure 3 Schematic diagram of the control process for the special ward status provided by the present invention;

[0042] Figure 4 Schematic diagram of the cold load prediction process for outpatient rooms provided by the present invention;

[0043] Figure 5 Schematic diagram of the multi-step prediction control optimization algorithm based on the MPC framework provided by the present invention. Specific implementation manners

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0045] The embodiments of the present invention disclose a method for refined energy-saving temperature control in hospital public buildings, as Figure 1 shown, including the following steps:

[0046] S1. Based on the usage requirements and energy consumption purposes of different wards, establish a refined hierarchical management architecture, including conventional inpatient wards, special wards, and outpatient wards. Special wards include the first type, the second type, and the third type of special wards;

[0047] S2. For conventional inpatient wards, establish a window magnetic control air-conditioning operation model. Based on the MPC model predictive control, establish a temperature and humidity predictive control model, a temperature and humidity - negative pressure joint predictive control model, and a temperature - negative pressure joint prediction model for different types of special wards, as well as a cold load prediction model for outpatient wards;

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

[0049] S4. For different types of special wards, according to the thermal parameters required to be controlled in the current type of ward, under the framework of the MPC model predictive control and using the multi-step prediction control method, regulate and optimize the corresponding thermal equipment;

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

[0051] To further implement the above technical solution, before step S1, connect the door and window body to the magnetic induction structure, the central temperature control structure, and the air-conditioning execution terminal in sequence. The model is designed to install the magnetic induction structure on the balcony aluminum alloy window of each room, and the magnetic induction structure uses a normally closed type; the switch part of the magnetic induction structure switch is configured with a lead wire, which is a signal wire. Lay the lead wire to the fan coil controller or control box, and the signal line uses a 2-core RS485 wire; when multiple window magnets are connected, select a 2-core RS485 wire and adopt a daisy-chain series connection method. The series-connected line is connected to the air-conditioning thermostat or the fan coil control box; the window magnet and the thermostat are connected through a 2-core RS485 wire.

[0052] To further implement the above technical solution, the specific content of the multi-step prediction control method is: by predicting the state parameters of conventional inpatient wards, special wards, and outpatient wards after multiple time steps, within each unit time step, select the minimum deviation from the prediction results as the target, and use the multi-objective particle swarm optimization algorithm to optimize the combined output of the equipment to obtain the optimal control results of the ward equipment.

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

[0054] Use the window magnetic sensor to detect the opening and closing of doors and windows in real time and the temperature control sensor to measure the temperature in real time, and calculate the actual deviation between the indoor actual temperature and the preset temperature;

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

[0056] If the deviation is large, it is judged that the user may have forgotten to close the doors and windows during a long-term absence. Keeping the doors and windows open for a long time will cause a large amount of air-conditioning cooling capacity to be wasted. To avoid this situation, the temperature and humidity detector will then feed back the deviation situation to the window magnetic sensor in real time, triggering a response alarm. If there is no response after the buffer time, the operation of the fan coil unit will be controlled to enforce the shutdown of the air conditioner.

[0057] If the deviation is small, it means that the opening and closing time of the doors and windows in the current period is short or the opening and closing amplitude is small. At this time, it may be that a patient is entering or leaving or there is short-term ventilation. In this case, the window magnetic actuator can output a signal to control the opening and closing of the doors and windows based on the optimal thermal situation predicted by the model. Specifically: based on the temperature deviation and the disturbance of the opening and closing frequency of the doors and windows, the window magnetic control air-conditioning operation model predicts and controls the opening and closing degree of the doors and windows. In each step of the prediction optimization, judge the positive and negative conditions of the current temperature difference, and predict the control and adjustment of the opening and closing degree of the doors and windows under different deviation conditions respectively. Repeat the prediction process in multiple steps to obtain all the control results of the window magnetic opening degree. The window magnetic switch has only two options: increase and decrease. In theory, after n steps, there are 2 n kinds. Through the particle swarm optimization algorithm to find the optimal solution among all the prediction results, obtain the optimal control result, and select the opening and closing situation of the doors and windows at the first step corresponding to the optimal control result as the actual door and window control.

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

[0059] S41. Detect the indoor and outdoor state parameters of the special ward through the differential pressure sensor, temperature sensor and humidity sensor, including the differential pressure in different areas of the special ward, the indoor temperature, and the moisture content and humidity of the exhaust air.

[0060] S42. Determine the type of the special ward to which it belongs. Using the deviation of the corresponding thermal parameters as the input, and combining the outdoor environmental state, the number of people flow parameters and the magnetic induction signal as disturbance factors, respectively through the temperature and humidity prediction control models corresponding to the first type, second type, and third type of special wards, the temperature and humidity-negative pressure combined prediction control model and the temperature-negative pressure combined prediction model, predict the output of the corresponding related equipment, and predict the state parameters of the next three steps each time.

[0061] S43. Add the thermal parameters to be controlled in the current type of special ward to the objective function according to the coefficient.

[0062] S44. Under the multi-step prediction, compare the particle swarm optimization algorithm in multiple cases to optimize the objective function to obtain 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 the relevant processing equipment that meet the requirements of each type of special ward.

[0063] In order to further implement the above technical solution, in step S42, the deviation of the corresponding thermal parameters is as follows: within each step, the indoor water vapor pressure, humidity content and enthalpy value under different states of the window magnetic switch are calculated and 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] In order to further implement the above technical scheme, the output prediction of the corresponding related equipment of the temperature and humidity prediction control model in step S42 is predicted as the output of the air-conditioning water-cooling unit, the output prediction of the corresponding related equipment of the temperature and humidity-negative pressure joint prediction control model is predicted as the output of the air-conditioning water-cooling unit, the air supply unit and the exhaust fan unit, and the output prediction of the corresponding related equipment of the temperature-negative pressure joint prediction model is predicted as the output of the air-conditioning, air supply unit and exhaust fan unit.

[0065] In order to further implement the above technical solution, each unit step prediction result has three control variables, namely, the compressor of the air-conditioning water-cooling unit, the air supply speed, and the valve opening of the air supply and exhaust. n prediction results.

[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 chiller and air conditioner operating conditions;

[0067] For stable temperature and humidity regulation, the controller is a PLC controller, which is connected to the temperature sensor, humidity sensor, opening and closing status sensor, and command receiver; a real-time display is set for the indoor thermal state to realize the visualization of the thermal parameters in the diseased state, and monitor the operating status information, fault information, and alarm signals in the ward; it is also connected to a historical data storage device;

[0068] In the coupled model predictive control of temperature, humidity and negative pressure load, the negative pressure control of the ward is carried out by the pressure difference sensor to monitor the pressure difference status in the ward in real time, and the monitoring situation is fed back to the central processor in real time. The central processor generates an execution command. After the command receiver receives the control signal, it adjusts the opening and closing size of the exhaust fan unit of the system to control the change of the air supply parameters in the ward, thereby controlling the negative pressure level in the ward; the pressure difference control is compared by setting the expected limit value, and according to the deviation between the real-time pressure difference and the pressure difference of each limit value, it is converted into the opening and closing signal of the air volume control valve to change the opening of the exhaust control valve;

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

[0070] In this embodiment, for the differential pressure control in the negative pressure ward, by setting different deviation ranges and adjusting the valve opening according to the current deviation range, different deviation ranges are set for high and low differential pressures respectively. For small deviations, an alarm signal is issued, and for large deviations, the exhaust valve is controlled to open or close according to the magnitude of the current required differential pressure.

[0071] The temperature and humidity control of the ward is achieved by the water-cooled unit of the air conditioner. For the temperature control of the ward, it is composed of a condenser, a compressor, an evaporator, a throttle valve, etc. These four components work together to form a complete refrigeration cycle. In the optimization, according to the temperature deviation as the input of the predictive control, the power of the compressor is adjusted; for the humidity control, it is also adjusted by the water-cooled unit, but in the air handling unit, it is different from the refrigeration condition. Generally, in the air handling part, the fan speed needs to be changed, and by changing the contact time between the air and the copper pipe, the air temperature is changed, thereby affecting the humidity of the air.

[0072] Specifically:

[0073] For the temperature and humidity control, the output of the water-cooled unit of the air conditioner is adjusted according to the following formulas respectively:

[0074] Calculate the saturated water vapor pressure:

[0075]

[0076] where t is the temperature;

[0077] Calculate the moisture content:

[0078]

[0079] where P sb is the saturated water vapor pressure, P is the atmospheric pressure, with a value of 101325, is the relative humidity;

[0080] Calculate the air enthalpy value:

[0081] h = (1.006 + 1.86×d)×t + 2501×d Leakage of the negative pressure ward room:

[0082]

[0083] where Q 漏 represents the leakage air volume, P represents the differential pressure, and A represents the area of doors, windows or gaps;

[0084] The approximate leakage air volume is obtained as the number of air changes:

[0085]

[0086] After considering the frequent door-opening disturbances, the exhaust air temperature of the air conditioning unit is predicted according to the current thermal parameters in the ward by substituting them into the following formula:

[0087]

[0088] Where, N is the number of air changes per hour, t 排i+Δt is the exhaust air temperature of the air conditioner in the next time period, t 标i is the target temperature of the room at the current moment, t 内i is the indoor temperature at the current moment, t 外i is the outdoor temperature at the current moment, Δt is the time step, M i is the occupancy situation of the ward in the current time period, is the heat dissipation coefficient of the patients in the ward;

[0089] Relationship between the air conditioning output power and the exhaust air temperature of the air conditioner:

[0090]

[0091] Where, Q 冷 is the cooling load of the air conditioner, Q 热 is the heating load of the air conditioner, T 内 is the indoor temperature, T 排 is the exhaust air temperature;

[0092] The cooling and heating loads of the air conditioner are:

[0093] Q 冷 = q m × (h 外 - h 露 )

[0094] Q 热 = q m × (h 排 - h 露 )

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

[0096] The temperature and humidity regulation of the ward is carried out through a fresh air air conditioning system. The energy consumption calculation formula of the air conditioning chiller is the same as that of the ward, and only the air change rate of the air conditioner needs to be flexibly controlled to avoid energy consumption loss.

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

[0098]

[0099] Where, ΔH i+Δtis the room humidity for the next time period, ΔS i is the heat absorbed by the room within the time step, k is the room heat transfer coefficient, H i is the current room humidity, H 0 is the humidity of the air exhausted by the air conditioner, v is the air exhaust velocity of the air conditioner, Δt is the time step, a, b are model parameters, which are continuously refined through iteration;

[0100] The relationship between the air conditioner fan power and the humidity of the air exhausted by the air conditioner is:

[0101]

[0102] Among them, P a is the standard atmospheric pressure, R is the ideal gas constant, T 排 is the exhaust temperature, E is the partial pressure of water vapor, Q is the air volume, ΔP is the pressure difference generated by the fan, η 1 is the fan efficiency.

[0103] After predicting and calculating the equipment output, under the MPC framework, an optimization algorithm is used to obtain the optimization result. As Figure 4 shown, during the calculation, the deviation between the actual and predicted values of each time step is used as the input to enter the constructed model for prediction. In each control period, a multi-objective particle swarm optimization algorithm is introduced, and the objective function constructed based on the addition coefficient is used to seek the optimal solution set, and the output result at the k+1 moment in the future period N is recorded in the system. Repeat this process to achieve the rolling optimization of the refined management of hospital equipment.

[0104] For the above control situation, under the framework of MPC, the output is predicted for multiple time steps, and the particle swarm optimization algorithm is used to optimize the minimum deviation among multiple results. The output of the first time step corresponding to the optimal result is used as the actual execution output.

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

[0106] S51. Monitor the current crowd density in the outpatient department through sensors as the disturbance input, and the outdoor temperature and the indoor temperature of the outpatient ward as the known state parameters in the prediction model. Obtain the preset temperature of the air conditioner according to the temperature comfort interval set by the season;

[0107] S52. Calculate through the cooling load prediction model to obtain the actual air conditioner output required to achieve the target temperature;

[0108] Cooling load prediction model:

[0109]

[0110] Among them, Pac i+ΔtThe predicted output of the air conditioner for the next time period, n peo，i is the population density of the outpatient department at the current moment, ω is the human body heat dissipation coefficient per unit time, T out，i is the outdoor temperature at the current moment, T in，i is the current indoor temperature, T pre，i is the expected stable temperature at the current moment, ρ air is the density of the air in the outpatient department, c air is the specific heat capacity of the air in the outpatient department, V r is the room volume of the outpatient department, β is the air conditioner energy efficiency ratio;

[0111] S53. According to the prediction model, predict the room temperature change after multiple time steps, and take the minimum value of the deviation between the predicted room temperature and the expected room temperature as the objective function. Under the particle swarm optimization algorithm, obtain the optimal air conditioner output result through optimization. Take the air conditioner output situation of the first time step as the actual air conditioner output for the next time period, update the temperature situation in the ward, enter the next time step, repeat the process, and complete the predictive control within the entire optimization time period.

[0112] A computer-readable storage medium, on which a computer program is stored, characterized in that when the computer program is executed by a processor, it implements a method for refined energy-saving control of the temperature of hospital-like public buildings.

[0113] A processing terminal, including a memory and a processor, where a computer program that can run on the processor is stored in the memory, characterized in that when the processor executes the computer program, it implements a method for refined energy-saving control of the temperature of hospital-like public buildings.

[0114] An embodiment of the present invention provides a method for refined energy-saving control of the temperature in hospital public buildings. Through the constructed Internet of Things system of doors and windows, sensors, and air-conditioning terminal execution ends, it is possible to synchronously monitor the thermal state parameters and the opening and closing conditions of doors and windows in the ward. By feeding the monitored data back into the energy management system in real time for calculation, through the model predictive control framework in the system, the working state of the window magnetic switch is predicted. At the same time, the ward data is transmitted to the preset task processing module in the central temperature control structure in real time through the data transmission network for processing, so as to realize the execution operation of the operating state of the air-conditioning terminal. In actual operation, the real-time deviation data of the thermal conditions in the ward will be synchronously uploaded to the central management system to achieve visual management operation. The execution terminal will make a judgment based on the deviation situation and output an execution signal to the window magnetic sensor. After receiving the signal, the window magnetic sensor will trigger a warning, and after a buffer time, the air-conditioning execution switch will be activated. Through the refined adjustment of the temperature and humidity in the ward by multi-device Internet of Things sensors and window magnetic devices, and at the same time through window magnetic control, the situation of the window being open for a long time during air-conditioning operation is avoided, saving energy while solving the problem of air-conditioning condensation; the multi-device Internet of Things to sensors increases the intelligent means of system operation, effectively realizing the automation and refinement of the overall system operation, and the entire system can effectively achieve the stable operation of temperature and humidity.

[0115] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and reference can be made to the description in the method part for related parts.

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

Claims

1. A refined energy-saving control method for temperature in a hospital-type public building, characterized in that: The following steps are involved: S1. Establish a refined hierarchical management structure based on the usage requirements and energy usage purposes of different wards, including regular inpatient wards, special wards and outpatient wards. Special wards include first, second and third category special wards; S2. Establish a window magnetic control air conditioning operation model for conventional inpatient wards. Based on the MPC model predictive control, establish temperature and humidity predictive control models for different types of special wards, a temperature and humidity-negative pressure combined predictive control model and a temperature-negative pressure combined predictive model, as well as a cooling load prediction model for outpatient wards; S3. For conventional inpatient wards, the data on the opening and closing status of doors and windows and the thermal status of the wards are collected, and the air conditioning operation is controlled and the opening and closing status of doors and windows are adjusted based on the window magnetic control air conditioning operation model and the multi-step predictive control method; S4. For different types of special wards, according to the thermal parameters that need to be controlled in the current type of ward, the corresponding thermal equipment is regulated and optimized using the multi-step predictive control method under the MPC model predictive control framework; S5. For outpatient wards, the outpatient air conditioning output operation is optimized based on the cooling load prediction model and the multi-step predictive control method according to the crowd density, temperature and indoor and outdoor temperature difference.

2. According to claim 1, a refined energy-saving control method for temperature of a hospital-type public building is characterized in that: Before step S1, the door and window bodies are connected to the magnetic induction structure, the central temperature control structure and the air conditioning execution terminal in sequence. The model proposes to install a magnetic induction structure on the balcony aluminum alloy window of each room, and the magnetic induction structure uses a normally closed type; 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 on the fan coil controller or control box, and the signal line uses a 2-core RS485 line; when multiple window magnets are connected, a 2-core RS485 line is selected, and a hand-in-hand series connection method is adopted. The series connection 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. According to claim 1, a refined energy-saving control method for temperature of a hospital-type public building is characterized in that: The specific content of the multi-step predictive control method is: by predicting the state parameters of conventional 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 equipment combination output to obtain the optimal control result of the ward equipment.

4. According to claim 1, a refined energy-saving control method for temperature of a hospital-type public building is characterized in that: The specific content of step S3 is: Use the window magnetic sensor to detect the opening and closing of doors and windows in real time and the temperature control sensor to measure the temperature in real time, and calculate the actual deviation between the actual room temperature and the preset room temperature; If the deviation is large, a response alarm is triggered. If there is no response after the buffer time, the air conditioner is forced to shut down by controlling the operation of the fan coil unit. If the deviation is small, based on the temperature deviation and the door and window opening and closing frequency disturbance, the window magnetic control air conditioning operation model is used to predict and control the door and window opening and closing degree. In each step of the prediction optimization, the positive and negative conditions of the current temperature difference are judged, and the control adjustment of the door and window opening and closing degrees under different deviation conditions are predicted respectively. The prediction process is repeated at multiple steps to obtain all the control results of the window magnetic opening. The particle swarm algorithm is used to optimize under all the prediction results to obtain the optimal control result, and the door and window opening and closing condition of the first step corresponding to the optimal control result is selected as the actual door and window control.

5. According to claim 1, a refined energy-saving control method for temperature of a hospital-type public building is characterized in that: The specific content of step S4 is: S41. Detecting indoor and outdoor status parameters of special wards through differential pressure sensors, temperature sensors and humidity sensors, including pressure differences in different areas of special wards, indoor temperature, and exhaust moisture content and humidity; S42. Determine the type of special ward, use the deviation of the corresponding thermal parameters as input, combine the outdoor environment status, human flow parameters and magnetic induction signals as disturbance factors, respectively use the temperature and humidity prediction control model corresponding to the first, second and third types of special wards, the temperature and humidity-negative pressure joint prediction control model and the temperature-negative pressure joint prediction model, to predict the output of the corresponding related equipment, and predict the state parameters of the next three steps each time; S43. Add the thermal parameters that need to be controlled for the current type of special ward to the objective function according to the coefficient; S44. Under multi-step prediction, the particle swarm algorithm in multiple cases is compared to optimize the objective function to obtain the minimum error as the output result, and the first prediction step of each optimal prediction result is used as the input signal, so as to obtain the optimization results of the relevant processing equipment that meet the needs of various types of special wards.

6. A refined energy-saving control method for temperature of a hospital-type public building according to claim 5, characterized in that: In step S42, the deviation of the corresponding thermal parameters is as follows: in each step, the indoor water vapor pressure, humidity content 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.

7. A refined energy-saving control method for temperature of a hospital-type public building according to claim 5, characterized in that: In step S42, the output prediction of the corresponding related equipment of the temperature and humidity prediction control model is predicted as the output of the air-conditioning water-cooling unit, the output prediction of the corresponding related equipment of the temperature and humidity-negative pressure joint prediction control model is predicted as the output of the air-conditioning water-cooling unit, the air supply unit and the exhaust fan unit, and the output prediction of the corresponding related equipment of the temperature-negative pressure joint prediction model is predicted as the output of the air-conditioning, air supply unit and exhaust fan unit.

8. According to claim 5, a refined energy-saving control method for temperature of a hospital-type public building is characterized in that: Each unit step prediction result has three control variables, namely, the compressor of the air-conditioning water-cooling unit, the air supply speed, and the valve opening of the air supply and exhaust. n prediction results.

9. The method for fine temperature energy-saving control of a hospital-type public building according to claim 5 is characterized in that: 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 chiller and air conditioning operating conditions; In the coupled model predictive control of temperature, humidity and negative pressure load, the negative pressure control of the ward is carried out by the pressure difference sensor which monitors the pressure difference status in the ward in real time and feeds back the monitoring situation to the central processor in real time. The central processor generates an execution command. After the command receiver receives the control signal, it adjusts the opening and closing size of the exhaust fan unit of the system to control the change of the air supply parameters in the ward, thereby controlling the negative pressure level in the ward. The pressure difference control is compared by setting up expected limit values, and according to the deviation between the real-time pressure difference and the pressure difference of each limit value, it is converted into the opening and closing signal of the air volume control valve to change the opening of the exhaust control valve.

10. The method for fine temperature energy-saving control of a hospital-type public building according to claim 1, characterized in that: The specific content of step S5 is: S51. The current flow density of people in the outpatient department is monitored by the sensor as the disturbance input, the outdoor temperature and the indoor temperature of the outpatient ward are used as the known state parameters in the prediction model, and the air conditioning preset temperature is obtained according to the seasonal temperature comfort range; S52. Calculate the actual air conditioning output required to achieve the target temperature through the cooling load prediction model; S53. According to the prediction model, the room temperature changes after multiple steps are predicted, and the minimum value of the deviation between the predicted room temperature and the expected room temperature is used as the objective function. Under the particle swarm algorithm, the optimal air-conditioning output result is obtained by optimization. The air-conditioning output of the first step is used as the actual air-conditioning output of the next period, the temperature in the ward is updated, and the next step is entered. The process is repeated to complete the prediction control within the entire optimization period.

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