An air conditioning system energy-saving control method and system based on steady-state identification

Abstract

The present application relates to the technical field of process control, in particular to an air conditioning system energy-saving control method based on steady state identification. The method comprises the following steps: S10, steady state identification of air conditioning operation data is performed based on a steady state identification method of DBSI of a difference equation to obtain a data interval of steady state; S20, a starting traversal search of each data interval of steady state is performed to find a COP maximum point of the air conditioning system under different steady state working conditions of fixed frequency operation, and the operation data of chilled water and cooling water under the COP maximum point are taken as optimal set points; S30, each optimal set point is controlled by using an MPC algorithm to save energy consumption and improve the performance of the air conditioning system. The present application uses a steady state identification method of an air conditioning system operation state, finds an optimal COP operation point by traversing and optimizing for the identified steady state interval based on operation data, and then uses the multi-input multi-output control characteristics of model prediction to solve the problems of multi-device coupling operation and control difficulty of the air conditioning system.

Description

Technical Field

[0001] The present invention relates to the technical field of process control, and in particular to an energy-saving control method for an air-conditioning system based on steady-state identification. Background Art

[0002] DBSI: A differential-signal based state identification method (DBSI) based on differential equations.

[0003] Model Predictive Control: Model Predictive Control (MPC) is an online optimization controller: at the sampling time, the current state is used as the initial state, and the system runs the model to predict the output changes in the future time domain, solve the optimal problem in the time domain, obtain the optimal sequence, and use the first value of the sequence as the input for control.

[0004] COP: The ratio of the cooling (heating) amount generated in the cooling (heating) cycle to the power consumed for cooling (heating).

[0005] OPC Protocol: OPC, short for OLE for Process Control, is an industry-standard protocol for process control. The OPC protocol is based on Microsoft's OLE (Object Linking and Embedding) technology and was developed by providing a set of standard OLE / COM interfaces. OPC utilizes OLE2 technology, which allows for the exchange of objects such as documents and graphics between multiple computers. The OPC protocol includes a standard set of interfaces, properties, and methods. The OPC protocol is widely used in process control systems, enabling interoperability between equipment and software from different vendors.

[0006] Under the current market situation and the demand for energy conservation, the energy-saving optimization control of air-conditioning systems has become a key research hotspot in the construction industry at home and abroad. The current problems with air-conditioning systems are that the air-conditioning circulating water system consumes a lot of energy, and the air-conditioning system is in a constant flow operation state for a long time. This shows that there are still many unreasonable aspects in the actual operation of the air-conditioning system. Because the water system lacks advanced control and regulation functions, when the water system is operating in a constant flow state, the required load of the water system and the operating load are not matched, resulting in a certain amount of energy consumption. During the operation of the air-conditioning system, due to the gradual change of temperature, the response speed of the system load demand is slow. The cooling water delivery of the water circulation system is often slower than the demand for changing chilled water cooling capacity. This may cause large fluctuations in the operating parameters of the air-conditioning control system and affect the control accuracy of the water system. In order to solve the above problems, an energy-saving control method for air-conditioning systems based on steady-state identification is proposed. Summary of the Invention

[0007] In order to solve the technical problems existing in the above-mentioned prior art, the present invention provides an energy-saving control method and system for an air-conditioning system based on steady-state identification.

[0008] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0009] In a first aspect, in one embodiment provided by the present invention, a method for energy-saving control of an air-conditioning system based on steady-state identification is provided, the method comprising the following steps:

[0010] S10, performing steady-state identification on the air-conditioning operation data using a steady-state identification method of DBSI based on a difference equation to obtain a steady-state data interval;

[0011] S20, traversing and searching for each steady-state data interval to find the COP maximum point of the air-conditioning system under different steady-state conditions under constant frequency operation, and using the chilled water and cooling water operation data at the COP maximum point as the optimal set point;

[0012] S30. Use the MPC algorithm to control the optimal set points, thereby improving the performance of the air-conditioning system while saving energy.

[0013] As a further solution of the present invention, the steady-state identification method of DBSI based on the differential equation in S10 performs steady-state identification on the air-conditioning operation data to obtain a steady-state data interval, including:

[0014] S101, reading operating data of air conditioning system sensors;

[0015] S102, selecting load Q from the operating data of the air conditioning system sensor as load data;

[0016] S103 : Perform steady-state identification on the load data Q of the day to obtain a steady-state data interval.

[0017] As a further solution of the present invention, the step S103 of performing steady-state identification on the load data Q of the day to obtain a steady-state data interval includes:

[0018] S1031. Perform differential processing on the load data Q of the day to obtain differential data of the operating data;

[0019] S1032. Determine the steady-state region amplitude by using a dichotomy method on the differential data;

[0020] S1033. Eliminate erroneous points identified in the differential data using a quadratic difference method.

[0021] As a further solution of the present invention, the step S20 of traversing and searching each steady-state data interval to find the COP maximum point of the air-conditioning system under different steady-state conditions under constant frequency operation, and using the chilled water and cooling water operation data at the COP maximum point as the optimal set point; includes:

[0022] S201, finding multiple steady-state interval data for the day;

[0023] S202, traversing COP data in each steady-state interval;

[0024] S203, finding the data points with the maximum COP in each steady-state interval;

[0025] S204: Record the maximum COP value in each steady-state interval.

[0026] As a further solution of the present invention, the operating data at least includes the cooling tower outlet water temperature, outdoor temperature, cooling tower inlet water temperature, chiller operating power, chiller cooling water outlet water temperature, chiller inlet water temperature, chilled water outlet water temperature, chilled water inlet water temperature, and system operation power consumption data of each equipment in the air-conditioning system on a certain day.

[0027] As a further solution of the present invention, the above S30, using the MPC algorithm to control the optimal set points, thereby improving the performance of the air-conditioning system while saving energy, includes:

[0028] S301, obtaining data of each node at each COP maximum value;

[0029] S302, using each node data as a device operation fixed point;

[0030] S303, using the MPC algorithm to control the reaching of the set point within each steady-state time of the day;

[0031] S304: Determine whether the operation for the day has ended. If so, proceed to S305; if not, return to S303;

[0032] S305: Update operation data.

[0033] In a second aspect, in another embodiment provided by the present invention, an air-conditioning system energy-saving control system based on steady-state identification is provided, and the system includes: a steady-state identification module, a traversal search module and an optimal control module.

[0034] The steady-state identification module performs steady-state identification on the air-conditioning operation data based on the steady-state identification method of DBSI of the differential equation to obtain a steady-state data interval.

[0035] The traversal search module starts traversal search for each steady-state data interval to find the COP maximum point of the air-conditioning system under different steady-state conditions under fixed frequency operation, and uses the chilled water and cooling water operation data at the COP maximum point as the optimal setting point.

[0036] The optimal control module uses the MPC algorithm to control the optimal set points, thereby improving the performance of the air-conditioning system while saving energy.

[0037] The technical solution provided by the present invention has the following beneficial effects:

[0038] The present invention utilizes a steady-state identification method for the operating status of an air-conditioning system. Based on operating data, the method traverses the identified steady-state interval to find the optimal COP operating point. Then, the multi-input and multi-output control characteristics predicted by the model are used to solve the problem of multi-device coupled operation and control difficulties in the air-conditioning system.

[0039] These and other aspects of the present invention will become more readily apparent in the following description of the embodiments. It should be understood that the above general description and the following detailed description are merely exemplary and explanatory and are not intended to limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other embodiments can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 The figure is a flow chart of an energy-saving control method for an air-conditioning system based on steady-state identification according to an embodiment of the present invention.

[0042] Figure 2 This is a detailed flow chart of an energy-saving control method for an air-conditioning system based on steady-state identification according to an embodiment of the present invention.

[0043] Figure 3 Graphs of original signals and differential signals in an air-conditioning system energy-saving control method based on steady-state identification according to an embodiment of the present invention.

[0044] Figure 4 This is a diagram of a differential signal and a steady-state determination threshold value in an air-conditioning system energy-saving control method based on steady-state identification according to an embodiment of the present invention.

[0045] Figure 5 This is a diagram of the original signal and the corrected state recognition result in an air-conditioning system energy-saving control method based on steady-state recognition according to an embodiment of the present invention.

[0046] Figure 6 This is a block diagram of an MPC control method for energy-saving control of an air-conditioning system based on steady-state identification according to an embodiment of the present invention.

[0047] Figure 7 This is a diagram showing the state identification results of load data in an air-conditioning system energy-saving control method based on steady-state identification according to an embodiment of the present invention.

[0048] Figure 8 This is a cooling water temperature difference control diagram in an air-conditioning system energy-saving control method based on steady-state identification according to an embodiment of the present invention.

[0049] Figure 9 This is a chilled water temperature difference control diagram in an air-conditioning system energy-saving control method based on steady-state identification according to an embodiment of the present invention.

[0050] Figure 10 The figure shows a comparison of the total electric power between fixed frequency and global adaptive in an energy-saving control method for an air-conditioning system based on steady-state identification according to an embodiment of the present invention.

[0051] Figure 11 This is a structural block diagram of an air-conditioning system energy-saving control system based on steady-state identification according to an embodiment of the present invention.

[0052] In the figure: 100-steady-state identification module, 200-traversal search module, 300-optimal control module. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0054] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0055] It should be understood that the terms used in this specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0056] Specifically, the embodiments of the present invention are further described below with reference to the accompanying drawings.

[0057] See also Figure 1 , Figure 1 This is a flow chart of an air-conditioning system energy-saving control method based on steady-state identification provided by an embodiment of the present invention. Figure 1 As shown, the air-conditioning system energy-saving control method based on steady-state identification includes steps S10 to S30.

[0058] S10. Perform steady-state identification on the air-conditioning operation data using a steady-state identification method of DBSI based on a differential equation to obtain a steady-state data interval.

[0059] In an embodiment of the present invention, the steady-state identification method of DBSI based on the differential equation in S10 performs steady-state identification on the air-conditioning operation data to obtain a steady-state data interval, including:

[0060] S101, reading operating data of air conditioning system sensors;

[0061] The operating data at least includes the cooling tower outlet water temperature, outdoor temperature, cooling tower inlet water temperature, chiller operating power, chiller cooling water outlet water temperature, chiller inlet water temperature, chilled water outlet water temperature, chilled water inlet water temperature, system operating power consumption and other data of each equipment in the air-conditioning system on a certain day.

[0062] S102, selecting load Q from the operating data of the air conditioning system sensor as load data;

[0063] S103 : Perform steady-state identification on the load data Q of the day to obtain a steady-state data interval.

[0064] In an embodiment of the present invention, the step S103 of performing steady-state identification on the load data Q of the day to obtain a steady-state data interval includes:

[0065] S1031. Perform differential processing on the load data Q of the day to obtain differential data of the operating data.

[0066] Exemplarily, S1031, performing differential processing on the load data Q of the day to obtain differential data of the operating data, includes:

[0067] The differential data of electric energy W is obtained by removing the burrs and missing points in the consumed electric energy data; then performing linear interpolation to supplement the missing points; and then performing forward difference on the processed consumed power data to obtain its differential data. Figure 3 shown.

[0068] S1032. Determine the steady-state region amplitude by performing a dichotomy on the differential data.

[0069] Exemplarily, S1032, determining the steady-state region amplitude by using a dichotomy method on the differential data, includes:

[0070] The specific implementation is to take THa=0 and THb=max|Xe| (where Xe is We) as the initial conditions, and calculate the data ratio P of the electric energy increment to be (THa+THb) / 2, then make THa=(THa+THb) / 2, otherwise make THb=(THa+THb) / 2, when |PP set |<StopError End judgment area continues to change. Judgment area amplitude is determined to be TH=THb. Figure 4 is the judgment result of the TH judgment threshold.

[0071] S1033: Eliminate erroneous points identified in the differential data using a quadratic difference method. The upper horizontal line (corresponding value is 1) and the lower horizontal line (corresponding value is 0) of the flag signal Flag of the steady-state identification result correspond to the transition state and the steady state, respectively.

[0072] Exemplarily, the step S1033 of eliminating erroneous points identified in the differential data using a quadratic difference method includes:

[0073] S10331, obtaining a differential signal We sequence of the steady-state determination signal Flag, extracting a point P in the amplitude sequence of the differential signal We that is equal to 1;

[0074] S10332. For the point sequence P obtained in the first step, find the point Q corresponding to the steady-state flag Flag, and take the number of 1s among the M points before and after the point. If the number of these points is greater than M, change the value of point Q to 1; otherwise, set it to 0.

[0075] S10333. Repeat steps S10331 and S10332 to update until the steady-state signal Flag does not change.

[0076] like Figure 5 , is the final steady-state identification result; when Flag = 2000, the system is in an unsteady state; when Flag = 0, the system is in a steady state.

[0077] S20 , traversing and searching each steady-state data interval to find the COP maximum point of the air-conditioning system under different steady-state conditions under constant frequency operation, and using the chilled water and cooling water operation data at the COP maximum point as the optimal setting point.

[0078] In an embodiment of the present invention, S20, starting to traverse and search for each steady-state data interval, finding the COP maximum point of the air-conditioning system under different steady-state conditions under fixed-frequency operation, and using the chilled water and cooling water operation data at the COP maximum point as the optimal set point; including:

[0079] S201, finding multiple steady-state interval data for the day;

[0080] S202, traversing COP data in each steady-state interval;

[0081] S203, finding the data points with the maximum COP in each steady-state interval;

[0082] S204: Record the maximum COP value in each steady-state interval.

[0083] S30. Use the MPC algorithm to control the optimal set points, thereby improving the performance of the air-conditioning system while saving energy.

[0084] In an embodiment of the present invention, S30, using the MPC algorithm to control the optimal set points, thereby improving the performance of the air conditioning system while saving energy, includes:

[0085] S301, obtaining data of each node at each COP maximum value;

[0086] S302, using each node data as a device operation fixed point;

[0087] S303, using the MPC algorithm to control the reaching of the set point within each steady-state time of the day;

[0088] In an embodiment of the present invention, the MPC algorithm is used to control reaching the set point, and its control process includes model prediction, rolling optimization and feedback correction.

[0089] In an embodiment of the present invention, the model prediction includes:

[0090] The prediction model is obtained through the operation data of the air-conditioning system equipment. The step response equation of the model is as follows:

[0091] X k+1 =AX k +Bu k (1)

[0092] Y k+1 =CX k+1 (2)

[0093] The unit step response model obtains the sampling value a of the step response i =a(iT), i=1, 2, ..., where T is the sampling period. When the step response is at a certain time t N =NT and then tends to be stable, a=[a1…a N ] Tis the model vector, and N is the modeling step size. At time k, assuming that the control system has initial predicted values Y0(k+i∣k) for the outputs in the next N time instants, where i = 1, …, N, under the action of M consecutive control increments Δu(k), Δu(k+1), …, Δu(k+m-1) starting from the current time instant, M is called the control step size and the predicted output values Y M is Equation (3):

[0094]

[0095] The predicted value should be as close as possible to the set value r p (k+i), where i = 1, …, P, and usually M < P < N. The above equation is expressed in vector form as Equation (4)

[0096] Y(k) = Y0(k) + AΔu m (k) (4)

[0097] In Equation (5), A is the unit step response coefficient a i [[ID=二十]]composed of a P×N matrix.

[0098]

[0099] In an embodiment of the present invention, the rolling optimization includes:[[]]

[0100] During MPC control, the real-time output Y(k+1) and the predicted output Y1(k+1|k) are collected at this time to obtain the output error e(k+1), as shown in Equation (6).

[0101] e(k+1) = Y(k+1) - Y1(k+1|k) (6)

[0102] Then, the error e(k+1) is weighted to obtain an error sequence to correct the MPC model, and its calculation expression is as follows (7):

[0103] y cor (k+1) = y N1 (k) + he(k+1) (7)

[0104] where y cor (k+1) is the predicted value of the corrected model output at the next time instant; h is the N-dimensional error weighting sequence. The online optimization problem at time K is that, under the action of Δu(k), Δu(k+1), …, Δu(k+m-1), the control system gradually reaches the set value at the next P time instants while ensuring that the system is in a stable state. The control performance index is written in vector form, that is, the performance optimization index can be expressed as the following Equation (8). Here, N and Q are weight matrices, and by adjusting the sizes of N and Q, the set error and energy consumption in the optimization algorithm can be optimized.

[0105]

[0106] In an embodiment of the present invention, the feedback correction includes:

[0107] Finally, y cor (k+1) Get y by shifting matrix A N0 (k+1) is used as the initial value of the model prediction at the next moment, and its calculation expression (9) is as follows:

[0108] y N0 (k+1)=Ay cor (k+1) (9)

[0109] At this time, a0 in the shift matrix A will act on the device as the real-time control quantity Δu(k), controlling the operation of the device so that the system runs to the set temperature.

[0110] S304: Determine whether the operation for the day has ended. If so, proceed to S305; if not, return to S303;

[0111] S305: Update operation data.

[0112] The present invention uses a steady-state identification algorithm to realize the identification of the operating status of the air-conditioning system, and a model predictive control algorithm to control the air-conditioning operating equipment to achieve energy-saving effects; the data is divided into steady-state data and transition state data through the difference method and the dichotomy method, and then the error points identified in the steady-state data are eliminated using the quadratic difference method; the state space equation of the equipment data is constructed through the system identification method to realize a multi-input and multi-output prediction model.

[0113] Exemplarily, the global steady-state optimization control process is described by taking the building system operation data points in Table 1 below as an example.

[0114] Table 1 System operation data

[0115]

[0116] Since COP is generally used as a performance indicator in air conditioning systems, the larger the COP, the better the performance of the air conditioning system. Therefore, the main purpose of this algorithm is to find the maximum COP point in the steady state during the operation of the air conditioning system. The specific execution steps are as follows:

[0117] 1. Read the sensor data of the system operation as shown in Table 1;

[0118] 2. Select the load Q in the air conditioning system operation data table as the algorithm operation data;

[0119] 3. Perform DBSI steady-state identification on load data Q;

[0120] 4. Assume that two steady-state intervals are found for the day (taking Table 1 as an example, the first three groups of data are steady-state 1, and the last three groups of data are steady-state 2. The following explanations are based on Table 4-1);

[0121] 5. Traverse the COP data in steady state 1 and steady state 2 in Table 1;

[0122] 6. Find the two COP maximum points of 3.91 and 3.42;

[0123] 7. Obtain the data for each node in steps 7 and 8 of the flowchart. The cooling tower outlet water temperature of the optimized control node group in interval 1 is 29.37 degrees, the chilled water temperature difference is 2.61 degrees, and the cooling water temperature difference is 3.12 degrees. No further details are given for interval 2.

[0124] 8. In flowchart steps 9 and 10, use the data points in step 7 as MPC set points;

[0125] 9. When the steady state interval 1 time period is reached on the same day, the first MPC set point is run; when the steady state interval 2 time period is reached on the same day, the second MPC set point is run

[0126] 10. If the steady-state interval for the day is not completed, continue running; if the run is completed, end after updating the day's running data.

[0127] The simulation time is set to 600 seconds, the sampling time is 0.4 seconds, and the building load data and the actual operating conditions of the MATLAB air-conditioning system are combined to change the air-conditioning system demand load. The air-conditioning system equipment operates at a fixed frequency. After automatic adjustment, the cooling water inlet and outlet temperatures, cooling water inlet and outlet temperatures, and air-conditioning system COP are recorded. The steady-state identification results of the air-conditioning system required load are as follows: Figure 7 As shown, where n=25, M=50, TH=186.13.

[0128] From above Figure 7 It was identified that the system has two steady-state operating time intervals: the 10-40 sampling point interval and the 51-329 sampling point interval. Data optimization was performed on these two intervals, with the maximum COP value as the optimization metric, to find the optimal performance point of the air conditioning system, which served as the system operation set point. Based on the optimization program, the optimal system COP for the two steady-state intervals was 4.42 and 4.05, the optimal set points for the cooling water return temperature were 28.03°C and 28.85°C, the optimal set points for the cooling water temperature difference were 2.12°C and 1.55°C, and the chilled water temperature difference was 1.77°C and 1.50°C.

[0129] Then, according to the established MPC controller, based on the principle that the cooling tower adjusts the cooling water return temperature, the cooling pump adjusts the cooling water temperature difference, and the refrigeration pump adjusts the cooling water temperature difference, the COP of the air-conditioning system is optimized and the overall performance of the air-conditioning system is improved.

[0130] Down Figure 8 and 9 They are the control effect diagrams of global steady-state optimization control of cooling water temperature difference, chilled water temperature difference, and so on.

[0131] like Figure 10 , showing a comparison of the total operating power of the air conditioning system equipment under fixed-frequency operation and global steady-state optimization control. The average total power consumed during the steady-state interval using the model predictive control method based on steady-state identification is 903.15 kW, while the average total power consumed during the steady-state interval under fixed-frequency operation is 911.30 kW.

[0132] It should be understood that, although the above is described in a certain order, these steps are not necessarily performed in sequence according to the above order. Unless there is clear explanation in this article, the execution of these steps does not have strict order restriction, and these steps can be performed in other orders. Moreover, a part of the steps of the present embodiment may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be performed in turn or alternately with at least a portion of the steps or stages in other steps or other steps.

[0133] In one embodiment, see Figure 11 As shown, an embodiment of the present invention further provides an air-conditioning system energy-saving control system based on steady-state identification. The system includes a steady-state identification module 100 , a traversal search module 200 and an optimal control module 300 .

[0134] The steady-state identification module 100 performs steady-state identification on the air-conditioning operation data based on the steady-state identification method of DBSI of the differential equation to obtain a steady-state data interval.

[0135] The traversal search module 200 starts traversal search for each steady-state data interval to find the COP maximum point of the air-conditioning system under different steady-state conditions under constant frequency operation, and uses the chilled water and cooling water operation data at the COP maximum point as the optimal setting point.

[0136] The optimal control module 300 uses the MPC algorithm to control the optimal set points, thereby improving the performance of the air conditioning system while saving energy.

[0137] It should be understood that, as used herein, the singular form "a" or "an" is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations of one or more of the items listed in association. The serial numbers of the embodiments disclosed in the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0138] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the disclosure of the embodiments of the present invention (including the claims) is limited to these examples. Within the spirit of the embodiments of the present invention, the technical features of the above embodiments or different embodiments may be combined, and there are many other variations of different aspects of the above embodiments of the present invention, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the scope of protection of the embodiments of the present invention.

Claims

1. An air conditioning system energy-saving control method based on steady-state identification, characterized in that: The method includes: S10, performing steady-state identification on the air-conditioning operation data using a steady-state identification method of DBSI based on a difference equation to obtain a steady-state data interval; The step S10, wherein the steady-state identification method of DBSI based on the differential equation performs steady-state identification on the air-conditioning operation data to obtain a steady-state data interval, includes: S101, reading operating data of air conditioning system sensors; S102, selecting load Q from the operating data of the air conditioning system sensor as load data; S103, performing steady-state identification on the load data Q of the day to obtain a steady-state data interval; S20, traversing and searching for each steady-state data interval to find the COP maximum point of the air-conditioning system under different steady-state conditions under constant frequency operation, and using the chilled water and cooling water operation data at the COP maximum point as the optimal set point; The step S20 starts traversing and searching for each steady-state data interval to find the COP maximum point of the air-conditioning system under different steady-state conditions under constant frequency operation, and uses the chilled water and cooling water operation data at the COP maximum point as the optimal set point; including: S201, finding multiple steady-state interval data for the day; S202, traversing COP data in each steady-state interval; S203, finding the data points with the maximum COP in each steady-state interval; S204: Record the maximum COP value of each steady-state interval S30, using the MPC algorithm to control the optimal set points, thereby improving the performance of the air conditioning system while saving energy; The step S30, using the MPC algorithm to control the optimal set points to improve the performance of the air conditioning system while saving energy, includes: S301, obtaining data of each node at each COP maximum value; S302, using each node data as a device operation fixed point; S303, using the MPC algorithm to control the reaching of the set point within each steady-state time of the day; S304: Determine whether the operation for the day has ended. If so, proceed to S305; if not, return to S303; S305: Update operation data.

2. The air conditioning system energy-saving control method based on steady-state identification according to claim 1, characterized in that: The step S103 of performing steady-state identification on the load data Q of the day to obtain a steady-state data interval includes: S1031. Perform differential processing on the load data Q of the day to obtain differential data of the operating data; S1032. Determine the steady-state region amplitude by using a dichotomy method on the differential data; S1033. Eliminate erroneous points identified in the differential data using a quadratic difference method.

3. The air conditioning system energy-saving control method based on steady-state identification according to claim 1, characterized in that: The operating data at least includes the cooling tower outlet water temperature, outdoor temperature, cooling tower inlet water temperature, chiller operating power, chiller cooling water outlet water temperature, chiller inlet water temperature, chilled water outlet water temperature, chilled water inlet water temperature, and system operating power consumption data of each device in the air-conditioning system on a certain day.

4. An air conditioning system energy-saving control system based on steady-state identification, characterized in that: The system includes a steady-state identification module, a traversal search module and an optimal control module, and the air-conditioning system energy-saving control method based on steady-state identification according to any one of claims 1 to 3 is applied to the air-conditioning system energy-saving control system based on steady-state identification; The steady-state identification module performs steady-state identification on the air-conditioning operation data based on the steady-state identification method of DBSI of the differential equation to obtain a steady-state data interval; The traversal search module starts traversal search for each steady-state data interval to find the COP maximum point of the air-conditioning system under different steady-state conditions under fixed frequency operation, and uses the chilled water and cooling water operation data at the COP maximum point as the optimal setting point; The optimal control module uses the MPC algorithm to control the optimal set points, thereby improving the performance of the air-conditioning system while saving energy.

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