A method and system for controlling energy saving of multiple hosts in a central air conditioning system

Abstract

The present invention discloses a multi-host collaborative energy-saving control method and system for a central air-conditioning system, comprising: obtaining the actual indoor temperature of a building in each time period of the first day; judging whether the host start-stop trigger condition is met through an event trigger strategy; using a PID algorithm when the trigger condition is met, determining the optimal host startup number according to the first error obtained by subtracting the reference supply and return water temperature difference at the current moment from the actual supply and return water temperature difference, and then using a start-stop host selection algorithm to select the start-stop host; subtracting the actual indoor temperature of the building in each time period of the first day from the reference indoor temperature to obtain the second error of the first day, using a PID type iterative learning control algorithm on the second day to determine the control strategy of the second day; using a PID type iterative learning control algorithm on the third day to determine the control strategy of the third day, so that error compensation is continuously performed through daily iteration cycles, so that the actual indoor temperature of the building eventually approaches an ideal value. The present invention can realize multi-host collaborative energy-saving control.

Description

Technical Field

[0001] The present invention relates to the technical field of heating, ventilation and air conditioning (HVAC) energy-saving control, and in particular to a method and system for collaborative energy-saving control of multiple hosts in a central air-conditioning system. Background Art

[0002] In recent years, with the implementation of energy conservation and emission reduction policies, HVAC (heat ventilation and air conditioning), especially the energy-saving control of central air conditioning systems, has received widespread attention. At present, central air conditioning control usually relies on water supply temperature set point adjustment and host start and stop. Effective control of water supply temperature set point and host start and stop is the key to reducing air conditioning energy consumption. However, the current set point adjustment and host start and stop in industry mainly rely on the experience of on-site operators and engineers. This manual operation cannot accurately determine the set point and is difficult to meet actual needs.

[0003] Optimal-Setting Control (OSC) is proposed to automatically adjust the set point of household air conditioners to improve the performance of local feedback control. OSC includes an outer loop control layer and an inner loop control layer, where the outer loop is used to adjust the set point of the inner loop controller. Unlike the traditional control system design method by changing the local controller itself, OSC improves the performance of the local controller by adding an outer loop set point adjustment loop without changing the local control system. The direct result of this is that it can avoid the waste of resources caused by replacing the control system, effectively improve the payback rate, and since there is no need to change the local control system, engineers will be more relaxed in actual management. However, for central air-conditioning systems containing multiple hosts, compared with household air conditioners, it is necessary to consider the coordinated start-stop and set point control of multiple hosts, but there are few related studies. Therefore, research on the coordinated start-stop and set point control of multiple hosts in central air-conditioning systems in existing buildings is of great significance to reducing system energy consumption.

[0004] In addition, the inner loop controller of the central air-conditioning main unit usually only uses the supply and return water temperature difference as an important basis for feedback control, without paying attention to the overall comfort of the building. This may cause huge fluctuations in indoor temperature, which will not only affect indoor comfort but also increase system energy consumption. Summary of the invention

[0005] The present invention provides a central air conditioning system multi-host collaborative energy-saving control method and system to solve the problems existing in the above-mentioned prior art. The technical solution is as follows:

[0006] On the one hand, a multi-host collaborative energy-saving control method for a central air-conditioning system is provided, comprising:

[0007] S1. Arrange temperature sensors to obtain the actual indoor temperature of the building at each time period on the first day;

[0008] S2, perform outer loop control, during which, according to the actual indoor temperature of the building, determine whether the host start-stop trigger condition is met through the event trigger strategy;

[0009] S3. When the trigger condition is met, the PID algorithm is used to determine the optimal number of hosts to be started at the next moment according to the first error obtained by calculating the difference between the reference supply and return water temperature difference at the current moment and the actual supply and return water temperature difference, and then the start-stop host selection algorithm is used to select the host to be started or stopped;

[0010] S4, calculating the difference between the actual indoor temperature of the building in each time period of the first day and the reference indoor temperature corresponding to each time period to obtain the second error of the first day, saving the second error of the first day and the initial host water supply setting temperature, and then using the PID iterative learning control algorithm on the second day to determine the control strategy for the second day according to the second error of the first day and the initial host water supply setting temperature;

[0011] S5. According to the control strategy for the second day, the host water supply set temperature for the second day is determined, the second error for the second day is obtained by subtracting the actual indoor temperature of the building in each time period on the second day from the reference indoor temperature corresponding to each time period, the second error for the second day and the host water supply set temperature for the second day are saved, and then on the third day, a PID iterative learning control algorithm is used to determine the control strategy for the third day according to the second error for the second day and the host water supply set temperature for the second day, so that error compensation is continuously performed through daily iteration cycles, and eventually the actual indoor temperature of the building approaches the ideal value.

[0012] Optionally, the formula of the event triggering strategy is as follows:

[0013]

[0014] in, is the time period, The actual indoor temperature of the building during the cycle is , for The actual indoor temperature of the building at the time, It is the event trigger adjustment factor. When the condition is met When the system The mechanism for adjusting the host start and stop status is triggered once, so the trigger time at this moment is , and record this time as , For the The time when the trigger condition is met.

[0015] Optionally, the start-stop host selection algorithm is as follows:

[0016] Ensure that the running time of each host is similar by giving priority to starting the host with the longest cumulative stop time and meeting the frequent start and stop restrictions, and by giving priority to stopping the host with the longest cumulative running time and meeting the frequent start and stop restrictions.

[0017] Optionally, the formula of the PID type iterative learning control algorithm is as follows:

[0018]

[0019] in, , , is the adjustable parameter of PID type iterative learning, For the The iteration cycle, The host water supply setting temperature at the moment, For the Iteration cycle, The host water supply setting temperature at the moment, For the Iteration cycle, The actual indoor temperature of the building at the moment With reference indoor temperature The difference is obtained The second error at the moment, For the Iteration cycle, The actual indoor temperature of the building at the moment With reference indoor temperature The difference is obtained The second error at the moment, Indicates Iteration cycle, starting from the host startup until the Sum all second errors at each moment.

[0020] Optionally, the actual indoor temperature of the building is the average temperature or median temperature of the building, which is determined according to the following formula:

[0021]

[0022] Wherein, T2 is the actual indoor temperature of the building, is the median temperature of each area in the building, Select the factor for temperature if ,but ,on the contrary, .

[0023] On the other hand, a central air conditioning system multi-host collaborative energy-saving control system is provided, the system comprising:

[0024] The temperature sensor is used to obtain the actual indoor temperature of the building at each time period on the first day;

[0025] The outer loop control cloud processor is used to perform outer loop control. During the outer loop control process, according to the actual indoor temperature of the building, the event trigger strategy is used to determine whether the host start-stop trigger condition is met; when the trigger condition is met, the PID algorithm is used to determine the optimal host startup number at the next moment according to the first error obtained by subtracting the reference supply and return water temperature difference at the current moment from the actual supply and return water temperature difference, and then the start-stop host selection algorithm is used to select the host to start or stop; the actual indoor temperature of the building in each time period of the first day is subtracted from the reference indoor temperature of each time period to obtain the second error of the first day, and the second error of the first day and the initial host water supply set temperature are saved, and then the PID type iteration algorithm is used on the second day. An iterative learning control algorithm is used to determine the control strategy for the second day according to the second error of the first day and the initial host water supply set temperature; the host water supply set temperature for the second day is determined according to the control strategy for the second day, and the second error for the second day is obtained by subtracting the actual indoor temperature of the building in each time period of the second day from the reference indoor temperature corresponding to each time period, and the second error for the second day and the host water supply set temperature for the second day are saved. Then, a PID iterative learning control algorithm is used on the third day to determine the control strategy for the third day according to the second error for the second day and the host water supply set temperature for the second day. In this way, error compensation is continuously performed through daily iteration cycles, so that the actual indoor temperature of the building eventually approaches the ideal value.

[0026] Optionally, the formula of the event triggering strategy is as follows:

[0027]

[0028] in, is the time period, The actual indoor temperature of the building during the cycle is , for The actual indoor temperature of the building at the time, It is the event trigger adjustment factor. When the condition is met When the system The mechanism for adjusting the host start and stop status is triggered once, so the trigger time at this moment is , and record this time as , For the The time when the trigger condition is met.

[0029] Optionally, the start-stop host selection algorithm is as follows:

[0030] Ensure that the running time of each host is similar by giving priority to starting the host with the longest cumulative stop time and meeting the frequent start and stop restrictions, and by giving priority to stopping the host with the longest cumulative running time and meeting the frequent start and stop restrictions.

[0031] Optionally, the formula of the PID type iterative learning control algorithm is as follows:

[0032]

[0033] in, , , is the adjustable parameter of PID type iterative learning, For the The iteration cycle, The host water supply setting temperature at the moment, For the Iteration cycle, The host water supply setting temperature at the moment, For the Iteration cycle, The actual indoor temperature of the building at the moment With reference indoor temperature The difference is obtained The second error at the moment, For the Iteration cycle, The actual indoor temperature of the building at the moment With reference indoor temperature The difference is obtained The second error at the moment, Indicates Iteration cycle, starting from the host startup until the Sum all second errors at each moment.

[0034] Optionally, the actual indoor temperature of the building is the average temperature or median temperature of the building, which is determined according to the following formula:

[0035]

[0036] Wherein, T2 is the actual indoor temperature of the building, is the median temperature of each area in the building, Select the factor for temperature if ,but ,on the contrary, .

[0037] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0038] The present invention, when implementing the collaborative energy-saving control of multiple hosts in the central air-conditioning system, respectively controls the start and stop of the host and the water supply temperature set point by introducing an event-triggered PID algorithm and a PID-type iterative learning control algorithm, which can replace the current extensive adjustment method that relies on the experience of engineers and realize lightweight and fully automatic energy-saving control. The PID parameters are easy to adjust. With the gradual iterative optimization on a daily basis, the control of the building temperature will be more accurate while achieving energy conservation and emission reduction. Moreover, this method is no longer implemented through local deployment, but uploads the real-time collected data to the outer loop control cloud processor, which processes the data and determines the control strategy, and sends it to the host through the protocol, realizing true cloud deployment and lightweight control. In addition, the present invention also uses the supply and return water temperature and the building temperature as the basis for strategy adjustment, which will greatly improve the control performance and energy-saving effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 This is a flow chart of a multi-host collaborative energy-saving control method for a central air-conditioning system provided by an embodiment of the present invention;

[0041] Figure 2 It is a schematic diagram of a multi-host collaborative energy-saving control method for a central air-conditioning system provided by an embodiment of the present invention;

[0042] Figure 3 It is a flow chart of a central air-conditioning system multi-host collaborative energy-saving control method running over time in a single day provided by an embodiment of the present invention;

[0043] Figure 4 It is a schematic diagram of a system hardware closed loop provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0045] like Figure 1 As shown, an embodiment of the present invention provides a flow chart of a method for collaborative energy-saving control of multiple hosts in a central air-conditioning system. The processing flow of the method may include the following steps:

[0046] S1. Arrange temperature sensors to obtain the actual indoor temperature of the building at each time period on the first day;

[0047] The embodiment of the present invention arranges a large number of temperature sensors, and ultimately feeds back the actual indoor temperature of the building to the outer loop controller (the embodiment of the present invention is an outer loop control cloud processor).

[0048] Optionally, the actual indoor temperature of the building is the average temperature or median temperature of the building, which is determined according to the following formula:

[0049]

[0050] Wherein, T2 is the actual indoor temperature of the building, is the median temperature of each area in the building, Select the factor for temperature if ,but ,on the contrary, .

[0051] S2, perform outer loop control, during which, according to the actual indoor temperature of the building, determine whether the host start-stop trigger condition is met through the event trigger strategy;

[0052] Optionally, the formula of the event triggering strategy is as follows:

[0053]

[0054] in, is the time period, The actual indoor temperature of the building during the cycle is , for The actual indoor temperature of the building at the time, It is the event trigger adjustment factor. When the condition is met When the system The mechanism for adjusting the start and stop status of the host is triggered once, so the trigger time at this moment is , and record this time as , For the The time when the trigger condition is met.

[0055] For example: When the host triggers the host start and stop condition once at 1 pm, this time is recorded as , and then the system continues to run. Assume that the host start and stop condition is triggered again at 3 pm. This time is recorded as , For two hours, if Hours, then .

[0056] S3. When the trigger condition is met, the PID algorithm is used to determine the optimal number of hosts to be started at the next moment according to the first error obtained by calculating the difference between the reference supply and return water temperature difference at the current moment and the actual supply and return water temperature difference, and then the start-stop host selection algorithm is used to select the host to be started or stopped;

[0057] The start and stop of the host is controlled by the PID algorithm. When the trigger condition is met, the PID algorithm is used to determine the optimal number of host startups at the next moment based on the first error obtained by calculating the difference between the reference supply and return water temperature difference at the current moment and the actual supply and return water temperature difference. Then, the start and stop host selection algorithm is used to select the host to start or stop.

[0058] Optionally, the start-stop host selection algorithm is as follows:

[0059] By giving priority to starting the host with the longest cumulative stop time and meeting the frequent start and stop limit (stop time is not less than a hours), and by giving priority to stopping the host with the longest cumulative running time and meeting the frequent start and stop limit (running time is not less than b hours), ensure that the running time of each host is similar.

[0060] For example: Assume that there are 10 hosts in total and the PID algorithm is Output 6 at all times, If the output is 5 at the moment, an additional host needs to be selected through the start-stop host selection algorithm to start.

[0061] S4, calculating the difference between the actual indoor temperature of the building in each time period of the first day and the reference indoor temperature corresponding to each time period to obtain the second error of the first day, saving the second error of the first day and the initial host water supply setting temperature, and then using the PID iterative learning control algorithm on the second day to determine the control strategy for the second day according to the second error of the first day and the initial host water supply setting temperature;

[0062] S5. According to the control strategy for the second day, the host water supply set temperature for the second day is determined, the second error for the second day is obtained by subtracting the actual indoor temperature of the building in each time period on the second day from the reference indoor temperature corresponding to each time period, the second error for the second day and the host water supply set temperature for the second day are saved, and then on the third day, a PID iterative learning control algorithm is used to determine the control strategy for the third day according to the second error for the second day and the host water supply set temperature for the second day, so that error compensation is continuously performed through daily iteration cycles, and eventually the actual indoor temperature of the building approaches the ideal value.

[0063] For set point temperature control, the embodiment of the present invention proposes a PID iterative learning control (ILC) algorithm in the outer loop control. The traditional PID algorithm is widely used in practical scenarios, but it has problems such as difficult parameter adjustment and poor control effect in the control of large and complex systems. In order to reduce the difficulty of parameter adjustment and prevent damage to the system, the embodiment of the present invention introduces a PID iterative learning control algorithm. Through a daily iteration cycle, error compensation is continuously performed, so that the average indoor temperature or median temperature of the building eventually approaches the ideal value.

[0064] Optionally, the formula of the PID type iterative learning control algorithm is as follows:

[0065]

[0066] in, , , is the adjustable parameter of PID type iterative learning, For the The iteration cycle, The host water supply set temperature at a certain moment (for example, if the iteration cycle is one day, For the first day, It is 9:00 a.m. That is, the host water supply set temperature at 9 am on the first day. The host water supply set temperature for each time period on the first day is usually set manually based on experience). For the Iteration cycle, The host water supply setting temperature at the moment, For the Iteration cycle, The actual indoor temperature of the building at the moment With reference indoor temperature The difference is obtained The second error at the moment (for example, if the iteration period is days, For the first day, It is 9:00 a.m. minute, It means the reference indoor temperature of the building at 9:30 am on the first day. It means the actual indoor temperature of the building at 9:30 am on the first day. With reference indoor temperature ), For the Iteration cycle, The actual indoor temperature of the building at the moment With reference indoor temperature The difference is obtained The second error at the moment, Indicates Iteration cycle, starting from the host startup until the Sum all second errors at that time (9 am).

[0067] The PID iterative learning control algorithm provided in the embodiment of the present invention is combined with the event-triggered PID algorithm to complete the construction of the outer loop controller (the embodiment of the present invention uses an outer loop control cloud processor), such as Figure 2 As shown, it replaces manual adjustment, greatly improves system operation efficiency, ensures building comfort, and reduces system energy consumption. Figure 2 In the example, the initial host water supply set temperature is given , then the inner loop control layer outputs the actual water supply temperature , Actual return water temperature and actual building temperature , the reference supply and return water temperature difference The actual supply and return water temperature difference Take the difference and get the first error Feedback is sent to the event-triggered PID algorithm. If an event is triggered (determined by the event trigger strategy), the PID algorithm is used to calculate the error according to the first error. Give the optimal number of hosts to start at the current moment, and then use the start-stop host selection algorithm to select the host to start or stop; set the actual indoor temperature of the building With reference indoor temperature Difference to get the second error , the second error and Save it to the register, and then use the PID iterative learning control algorithm on the second day to calculate the second error according to the first day. Give the control strategy for the next day.

[0068] Figure 3 This is a single-day operation flow chart of a multi-host collaborative energy-saving control method for a central air-conditioning system according to an embodiment of the present invention. Usually, the central air-conditioning system will be turned on at a fixed time. After turning on, the system will Figure 3 The process in the closed loop control is as follows:

[0069] When running on the first day, first make an initial run judgment. If it is the first run of the day, you need to obtain the initial state of the system (actual water supply temperature of the host , Actual return water temperature of the main engine , Actual indoor temperature of the building ) and the initial host start and stop sequence (determined based on actual experience), and then the day's sequence until the first day is finished, the second error All are saved in the register. When running the next day, first make the initial operation judgment. If it is the first operation of the next day, you need to obtain the initial state of the system (the actual water supply temperature of the host , Actual return water temperature of the main engine , Actual indoor temperature of the building ) and the initial host start-stop sequence (determined based on actual experience), and then use the PID iterative learning control algorithm to determine the second day's error based on the first day's second error. Moment Then determine whether the termination condition is met. The termination condition is generally whether it is time to get off work. If it is met, the air conditioning system is turned off. If not, the current system status (actual water supply temperature of the host) is , Actual return water temperature of the main engine , Actual indoor temperature of the building ) feedback, and judge whether the host start-stop trigger condition is met. If not, keep The host start and stop sequence at the time, if reached, is given by combining PID with the start and stop host selection algorithm Then the PID iterative learning control algorithm is used to give the host start and stop sequence at the moment. Moment , then update the system status and save the second error to the register, and cycle in sequence until the algorithm is shut down at the end of the shift.

[0070] Optionally, the embodiment of the present invention makes changes on the hardware side to ensure the timely issuance of control strategies and the real-time collection and processing of feedback data. The traditional energy-saving renovation of buildings is to replace the original high-energy-consuming equipment. This method has high initial investment costs, great construction difficulty, long cycle, and will also affect the normal operation of the building. Therefore, the traditional method has huge limitations. The embodiment of the present invention proposes a lightweight renovation plan to address the shortcomings of traditional building energy-saving renovation. Lightweight is reflected in the following aspects:

[0071] 1. The equipment is lightweight, and there is no need to replace the original air-conditioning host. Instead, it is achieved by connecting the protocol and arranging temperature sensors on the basis of the original equipment;

[0072] 2. Lightweight construction. Traditional energy-saving renovations require the layout of a large number of lines to ensure the normal operation of equipment at each point, and the transmission position is limited. Only part of the data in the computer room can be used as a reference for automatic control and adjustment of the system, and the status of the building office area cannot be used as system feedback. The reason is that long-distance wiring has huge construction difficulties. The embodiment of the present invention changes the original long-distance wiring method and uses wireless devices for long-distance data transmission and communication, which greatly reduces the complexity of construction and has little impact on the system itself.

[0073] 3. Lightweight management. The original management is bloated, requiring multiple people to take turns on duty in the equipment room to manage different systems locally, including fire protection, power supply, air conditioning and other key systems. The method adopted in the embodiment of the present invention is to synchronize all data to the cloud processor, process it in the cloud processor, and then directly control the unit by sending the data without manual intervention, and engineers can view the system operation status in real time in the software without having to sit in the office. With the support of the cloud processor, multiple systems are integrated together, and the same manager can observe the operation status of multiple systems at the same time, greatly improving management efficiency.

[0074] The outer loop operation hardware system of the embodiment of the present invention ensures that Figure 4 As shown. First, various sensors (the control method of the embodiment of the present invention requires a temperature sensor) collect data and transmit it to the cloud brain through a wireless gateway. After that, the cloud analyzes and processes the data, gives a reasonable control strategy for the next moment, and transmits it to the air conditioner host through the air conditioner host protocol interface to achieve outer loop closed-loop control. The data processing and control strategy generation in this method are all calculated in the cloud, so it does not require the computing power of the local device, which can greatly reduce the investment cost while achieving energy conservation and emission reduction, which is of great significance.

[0075] The embodiment of the present invention further provides a central air conditioning system multi-host collaborative energy-saving control system, the system comprising:

[0076] The temperature sensor is used to obtain the actual indoor temperature of the building at each time period on the first day;

[0077] The outer loop control cloud processor is used to perform outer loop control. During the outer loop control process, according to the actual indoor temperature of the building, the event trigger strategy is used to determine whether the host start-stop trigger condition is met; when the trigger condition is met, the PID algorithm is used to determine the optimal host startup number at the next moment according to the first error obtained by subtracting the reference supply and return water temperature difference at the current moment from the actual supply and return water temperature difference, and then the start-stop host selection algorithm is used to select the host to start or stop; the actual indoor temperature of the building in each time period of the first day is subtracted from the reference indoor temperature of each time period to obtain the second error of the first day, and the second error of the first day and the initial host water supply set temperature are saved, and then the PID type iteration algorithm is used on the second day. An iterative learning control algorithm is used to determine the control strategy for the second day according to the second error of the first day and the initial host water supply set temperature; the host water supply set temperature for the second day is determined according to the control strategy for the second day, and the second error for the second day is obtained by subtracting the actual indoor temperature of the building in each time period of the second day from the reference indoor temperature corresponding to each time period, and the second error for the second day and the host water supply set temperature for the second day are saved. Then, a PID iterative learning control algorithm is used on the third day to determine the control strategy for the third day according to the second error for the second day and the host water supply set temperature for the second day. In this way, error compensation is continuously performed through daily iteration cycles, so that the actual indoor temperature of the building eventually approaches the ideal value.

[0078] Optionally, the formula of the event triggering strategy is as follows:

[0079]

[0080] in, is the time period, The actual indoor temperature of the building during the cycle is , for The actual indoor temperature of the building at the time, It is the event trigger adjustment factor. When the condition is met When the system The mechanism for adjusting the start and stop status of the host is triggered once, so the trigger time at this moment is , and record this time as , For the The time when the trigger condition is met.

[0081] Optionally, the start-stop host selection algorithm is as follows:

[0082] Ensure that the running time of each host is similar by giving priority to starting the host with the longest cumulative stop time and meeting the frequent start and stop restrictions, and by giving priority to stopping the host with the longest cumulative running time and meeting the frequent start and stop restrictions.

[0083] Optionally, the formula of the PID type iterative learning control algorithm is as follows:

[0084]

[0085] in, , , is the adjustable parameter of PID type iterative learning, For the The iteration cycle, The host water supply setting temperature at the moment, For the Iteration cycle, The host water supply setting temperature at the moment, For the Iteration cycle, The actual indoor temperature of the building at the moment With reference indoor temperature The difference is obtained The second error at the moment, For the Iteration cycle, The actual indoor temperature of the building at the moment With reference indoor temperature The difference is obtained The second error at the moment, Indicates Iteration cycle, starting from the host startup until the Sum all second errors at each moment.

[0086] Optionally, the actual indoor temperature of the building is the average temperature or median temperature of the building, which is determined according to the following formula:

[0087]

[0088] Wherein, T2 is the actual indoor temperature of the building, is the median temperature of each area in the building, Select the factor for temperature if ,but ,on the contrary, .

[0089] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-host collaborative energy-saving control method for a central air-conditioning system, characterized in that: The method comprises: S1. Arrange temperature sensors to obtain the actual indoor temperature of the building at each time period on the first day; S2, perform outer loop control, during which, according to the actual indoor temperature of the building, determine whether the host start-stop trigger condition is met through the event trigger strategy; S3. When the trigger condition is met, the PID algorithm is used to determine the optimal number of hosts to be started at the next moment according to the first error obtained by calculating the difference between the reference supply and return water temperature difference at the current moment and the actual supply and return water temperature difference, and then the start-stop host selection algorithm is used to select the host to be started or stopped; S4, calculating the difference between the actual indoor temperature of the building in each time period of the first day and the reference indoor temperature corresponding to each time period to obtain the second error of the first day, saving the second error of the first day and the initial host water supply setting temperature, and then using the PID iterative learning control algorithm on the second day to determine the control strategy for the second day according to the second error of the first day and the initial host water supply setting temperature; S5. According to the control strategy for the second day, determine the host water supply set temperature for the second day, calculate the difference between the actual indoor temperature of the building in each time period of the second day and the reference indoor temperature corresponding to each time period to obtain the second error for the second day, save the second error for the second day and the host water supply set temperature for the second day, and then use the PID iterative learning control algorithm on the third day to determine the control strategy for the third day according to the second error for the second day and the host water supply set temperature for the second day, so that the error compensation is continuously performed through the daily iteration cycle, and finally the actual indoor temperature of the building tends to the ideal value; The formula of the event triggering strategy is as follows: ； in, is the time period, The actual indoor temperature of the building during the cycle is , for The actual indoor temperature of the building at the time, It is the event trigger adjustment factor. When the condition is met When the system The mechanism for adjusting the start and stop status of the host is triggered once, so the trigger time at this moment is , and record this time as , For the The time at which the trigger condition is met.

2. The method according to claim 1, characterized in that The start-stop host selection algorithm is as follows: Ensure that the running time of each host is similar by giving priority to starting the host with the longest cumulative stop time and meeting the frequent start and stop restrictions, and by giving priority to stopping the host with the longest cumulative running time and meeting the frequent start and stop restrictions.

3. The method according to claim 1, characterized in that The formula of the PID type iterative learning control algorithm is as follows: ； in, , , is the adjustable parameter of PID type iterative learning, For the The iteration cycle, The host water supply setting temperature at the moment, For the Iteration cycle, The host water supply setting temperature at the moment, For the Iteration cycle, The actual indoor temperature of the building at the moment With reference indoor temperature The difference is obtained The second error at the moment, For the Iteration cycle, The actual indoor temperature of the building at the moment With reference indoor temperature The difference is obtained The second error at the moment, Indicates Iteration cycle, starting from the host startup until the Sum all second errors at each moment.

4. The method according to claim 1, characterized in that The actual indoor temperature of the building is the average temperature or median temperature of the building, which is determined according to the following formula: ； in, T 2 is the actual indoor temperature of the building, is the median temperature of each area in the building, Select the factor for temperature if ,but ,on the contrary, .

5. A multi-host collaborative energy-saving control system for a central air-conditioning system, characterized in that: The system comprises: The temperature sensor is used to obtain the actual indoor temperature of the building at each time period on the first day; The outer loop control cloud processor is used to perform outer loop control. During the outer loop control process, according to the actual indoor temperature of the building, the event trigger strategy is used to determine whether the host start-stop trigger condition is met; when the trigger condition is met, the PID algorithm is used to determine the optimal host startup number at the next moment according to the first error obtained by subtracting the reference supply and return water temperature difference at the current moment from the actual supply and return water temperature difference, and then the start-stop host selection algorithm is used to select the host to start or stop; the actual indoor temperature of the building in each time period of the first day is subtracted from the reference indoor temperature of each time period to obtain the second error of the first day, and the second error of the first day and the initial host water supply set temperature are saved, and then the PID type iteration algorithm is used on the second day. An iterative learning control algorithm is used to determine the control strategy for the second day according to the second error of the first day and the initial host water supply set temperature; the host water supply set temperature for the second day is determined according to the control strategy for the second day, the second error for the second day is obtained by subtracting the actual indoor temperature of the building in each time period of the second day from the reference indoor temperature corresponding to each time period, the second error for the second day and the host water supply set temperature for the second day are saved, and then a PID iterative learning control algorithm is used on the third day to determine the control strategy for the third day according to the second error for the second day and the host water supply set temperature for the second day, so that the error compensation is continuously performed through daily iteration cycles, and finally the actual indoor temperature of the building approaches the ideal value; The formula of the event triggering strategy is as follows: ； in, is the time period, The actual indoor temperature of the building during the cycle is , for The actual indoor temperature of the building at the time, It is the event trigger adjustment factor. When the condition is met When the system The mechanism for adjusting the start and stop status of the host is triggered once, so the trigger time at this moment is , and record this time as , For the The time at which the trigger condition is met.

6. The system according to claim 5, characterized in that The start-stop host selection algorithm is as follows: Ensure that the running time of each host is similar by giving priority to starting the host with the longest cumulative stop time and meeting the frequent start and stop restrictions, and by giving priority to stopping the host with the longest cumulative running time and meeting the frequent start and stop restrictions.

7. The system according to claim 5, characterized in that The formula of the PID type iterative learning control algorithm is as follows: ； in, , , is the adjustable parameter of PID type iterative learning, For the The iteration cycle, The host water supply setting temperature at the moment, For the Iteration cycle, The host water supply setting temperature at the moment, For the Iteration cycle, The actual indoor temperature of the building at the moment With reference indoor temperature The difference is obtained The second error at the moment, For the Iteration cycle, The actual indoor temperature of the building at the moment With reference indoor temperature The difference is obtained The second error at the moment, Indicates Iteration cycle, starting from the host startup until the Sum all second errors at each moment.

8. The system according to claim 5, characterized in that The actual indoor temperature of the building is the average temperature or median temperature of the building, which is determined according to the following formula: ； in, T 2 is the actual indoor temperature of the building, is the median temperature of each area in the building, Select the factor for temperature if ,but ,on the contrary, .

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