An online hydraulic regulation method and system with adaptive identification correction

Abstract

This invention relates to the field of hydraulic regulation technology, and in particular to an adaptive identification and correction online hydraulic regulation method and system. The method includes: establishing a hydraulic condition model corresponding to preset operating conditions of the heating system; selecting a corresponding correction method based on the preset operating conditions; optimizing the hydraulic condition model according to the correction method; establishing a prediction model; determining a target valve opening based on the prediction model and the optimized hydraulic condition model; adjusting the actual valve opening to the target valve opening; and determining whether the actual flow rate is within a preset flow range. This invention solves the problem that existing hydraulic condition models cannot closely approximate the actual operating conditions of the heating network, resulting in an imbalance between the actual heating flow rate and the design flow rate, leading to energy-consuming and time-consuming hydraulic regulation.

Description

Technical Field

[0001] This invention relates to the field of hydraulic regulation, and in particular to an adaptive identification and correction online hydraulic regulation method and system. Background Technology

[0002] Currently, heating systems generally suffer from high energy consumption and low efficiency. The main problem with traditional extensive centralized heating is hydraulic imbalance, which leads to uneven flow distribution in each loop, resulting in uneven room temperature for each user.

[0003] In existing technologies, hydraulic operating conditions of heating systems are modeled to reflect the flow rate, pressure, and velocity of the pipeline network. However, existing hydraulic operating condition models cannot closely approximate the actual operating conditions of the heating network. The actual heating flow rate is unbalanced with the design flow rate, resulting in energy-consuming and time-consuming hydraulic regulation. Therefore, how to improve the accuracy of hydraulic operating condition models is a technical problem that needs to be solved. Summary of the Invention

[0004] To address the problems existing in the prior art, the purpose of this invention is to provide an adaptive identification and correction online hydraulic regulation method and system, which solves the problem that existing hydraulic operating condition models cannot approximate the actual operating conditions of the heating network, and the imbalance between the actual heating flow and the design flow leads to energy-consuming and time-consuming hydraulic regulation.

[0005] To achieve the above objectives, the present invention provides an adaptive identification and correction online hydraulic regulation method and system, the method comprising:

[0006] Establish a hydraulic condition model corresponding to the preset operating conditions of the heating system;

[0007] Select the appropriate correction method based on the preset operating conditions, and optimize the hydraulic condition model according to the correction method;

[0008] Establish a prediction model, determine the target valve opening based on the prediction model and the optimized hydraulic condition model, adjust the actual valve opening to the target valve opening, and determine whether the actual flow rate is within the preset flow range.

[0009] In some embodiments of this application, before selecting the corresponding correction method based on preset operating conditions, the method further includes:

[0010] Historical operating data is acquired, and the historical operating data is divided into input data and output data. The input data is then input into the corresponding hydraulic condition model to obtain the model data.

[0011] The historical operating data includes the supply water temperature, supply water pressure, valve opening, valve flow rate, return water temperature, and return water pressure of multiple pipeline flow regulating valves. The supply and return water temperature difference is determined based on the supply water temperature and return water temperature, and the supply and return water pressure difference is determined based on the supply water pressure and return water pressure.

[0012] The supply and return water temperature difference and supply and return water pressure difference are classified as input data, and the valve opening degree and valve flow rate are classified as output data.

[0013] In some embodiments of this application, a corresponding correction method is selected based on preset operating conditions, and the hydraulic condition model is optimized according to the correction method, including:

[0014] When the preset operating condition is static hydraulic condition:

[0015] Determine whether the difference between the model data and the output data is greater than a preset correction critical interval. If it is greater than the preset correction critical interval, correct the hydraulic condition model according to the first correction formula to obtain the first corrected model data. The model data includes the model valve opening degree and the model valve flow rate value. The difference between the model data and the output data includes the opening degree difference and the flow rate difference. The preset correction critical interval includes (opening degree correction critical value and flow rate correction critical value).

[0016] The first corrected formula is:

[0017]

[0018] Where T is the valve opening, T0 is the model valve opening, Tt is the opening correction critical value, Q is the valve flow rate, Q0 is the model valve flow rate, Qq is the flow rate correction critical value, and α is the hydraulic condition model loss rate.

[0019] When the preset operating condition is dynamic hydraulic condition:

[0020] The dynamic influence coefficient is determined according to the dynamic influence formula. The input data is then corrected according to the dynamic influence coefficient to obtain the corrected input data. The corrected input data is then input into the hydraulic condition model to obtain the second corrected model data.

[0021] The first preset input critical interval (R1, P1), the first preset input critical interval (R2, P2), and the third preset input critical interval (R3, P3) are determined according to the dynamic influence formula, and R1 < R2 < R3, P1 < P2 < P3; the corresponding dynamic influence parameters are selected according to the relationship between each preset input critical interval and the supply and return water temperature difference R0 and the supply and return water pressure difference P0.

[0022] When R0 < R1 and P0 < P1, the dynamic influence coefficient is β1 = k1 + n1 × (R0 + P0);

[0023] When R1≤R0<R2 and P1≤P0<P2, the dynamic influence coefficient is β2=k2+n2×(R0+P0);

[0024] When R2≤R0<R3 and P2≤P0<P3, the dynamic influence coefficient is β3=k3+n3×(R0+P0);

[0025] When R3≤R0 and P3≤P0, the dynamic influence coefficient is β4=βmax.

[0026] Where k1, k2, k3, n1, n2, n3 are all pre-defined constants;

[0027] The corrected input data is:

[0028]

[0029]

[0030] Where Re is the corrected supply and return water temperature difference, βi is the dynamic influence coefficient (i=1,2,3,4), and Pe is the corrected supply and return water pressure difference;

[0031] Input Re and Pe into the hydraulic condition model to obtain the second corrected model data;

[0032] The first and second modified model data are subtracted from the corresponding output data to obtain the first difference and the second difference. If the first difference and the second difference are less than the preset difference threshold, the optimized hydraulic condition model is obtained. If the first difference and the second difference are not less than the preset difference threshold, the hydraulic condition model is further modified until it is less than the preset difference threshold, at which point the modification stops.

[0033] In some embodiments of this application, a prediction model is established, and the target valve opening is determined based on the prediction model and the optimized hydraulic condition model, including:

[0034] The prediction model is a time series prediction algorithm model, which is used to predict the supply and return water temperature difference and supply and return water pressure difference of multiple pipeline flow regulating valves.

[0035] The predicted supply and return water temperature difference and supply and return water pressure difference are input into the optimized hydraulic condition model. With the goal of achieving flow balance within a preset time, the target valve opening of multiple pipeline flow regulating valves is output.

[0036] Adjust the actual valve opening to the target valve opening to obtain the actual flow rate value;

[0037] Determine whether the actual flow rate is within the preset flow rate range. If it is not within the preset flow rate range, determine whether the heating system is faulty. If the heating system is not faulty, adjust the actual valve opening until the actual flow rate is within the preset flow rate range. If the heating system is faulty, send an alarm signal.

[0038] In some embodiments of this application, determining whether the heating system is faulty includes:

[0039] Determine the transient pressure change rate when the heating system malfunctions based on historical operating data, and determine the fault range and fault type based on the transient pressure change rate;

[0040] The system acquires pressure values ​​at multiple pipeline flow regulating valves and calculates the transient rate of pressure change. It then determines whether the transient rate of pressure change is within the fault range. If it is within the fault range, an alarm signal is sent, which includes the fault location and fault type.

[0041] In some embodiments of this application, an adaptive identification and correction online hydraulic regulation system is also included:

[0042] A module is established to create a hydraulic condition model corresponding to the preset operating conditions of the heating system.

[0043] The correction module is used to select the appropriate correction method according to the preset operating conditions and optimize the hydraulic condition model according to the correction method.

[0044] The adjustment module is used to establish a prediction model, determine the target valve opening based on the prediction model and the optimized hydraulic condition model, adjust the actual valve opening to the target valve opening, and determine whether the actual flow rate is within the preset flow range.

[0045] In some embodiments of this application, before selecting the corresponding correction method based on preset operating conditions, the method further includes:

[0046] Historical operating data is acquired, and the historical operating data is divided into input data and output data. The input data is then input into the corresponding hydraulic condition model to obtain the model data.

[0047] The historical operating data includes the supply water temperature, supply water pressure, valve opening, valve flow rate, return water temperature, and return water pressure of multiple pipeline flow regulating valves. The supply and return water temperature difference is determined based on the supply water temperature and return water temperature, and the supply and return water pressure difference is determined based on the supply water pressure and return water pressure.

[0048] The supply and return water temperature difference and supply and return water pressure difference are classified as input data, and the valve opening degree and valve flow rate are classified as output data.

[0049] In some embodiments of this application, a corresponding correction method is selected based on preset operating conditions, and the hydraulic condition model is optimized according to the correction method, including:

[0050] When the preset operating condition is static hydraulic condition:

[0051] The correction module is used to determine whether the difference between the model data and the output data is greater than a preset correction critical interval. If it is greater than the preset correction critical interval, the hydraulic condition model is corrected according to the first correction formula to obtain the first corrected model data. The model data includes the model valve opening degree and the model valve flow rate value. The difference between the model data and the output data includes the opening degree difference and the flow rate difference. The preset correction critical interval includes (opening degree correction critical value, flow rate correction critical value).

[0052] The first corrected formula is:

[0053]

[0054] Where T is the valve opening, T0 is the model valve opening, Tt is the opening correction critical value, Q is the valve flow rate, Q0 is the model valve flow rate, Qq is the flow rate correction critical value, and α is the hydraulic condition model loss rate.

[0055] When the preset operating condition is dynamic hydraulic condition:

[0056] The correction module is used to determine the dynamic influence coefficient according to the dynamic influence formula, correct the input data according to the dynamic influence coefficient, obtain the corrected input data, and input the corrected input data into the hydraulic condition model to obtain the second corrected model data.

[0057] The correction module is also used to determine the first preset input critical interval (R1, P1), the first preset input critical interval (R2, P2), and the third preset input critical interval (R3, P3) according to the dynamic influence formula, where R1 < R2 < R3 and P1 < P2 < P3; and to select the corresponding dynamic influence parameters according to the relationship between each preset input critical interval and the supply and return water temperature difference R0 and the supply and return water pressure difference P0.

[0058] When R0 < R1 and P0 < P1, the dynamic influence coefficient is β1 = k1 + n1 × (R0 + P0);

[0059] When R1≤R0<R2 and P1≤P0<P2, the dynamic influence coefficient is β2=k2+n2×(R0+P0);

[0060] When R2≤R0<R3 and P2≤P0<P3, the dynamic influence coefficient is β3=k3+n3×(R0+P0);

[0061] When R3≤R0 and P3≤P0, the dynamic influence coefficient is β4=βmax.

[0062] Where k1, k2, k3, n1, n2, n3 are all pre-defined constants;

[0063] The corrected input data is:

[0064]

[0065]

[0066] Where Re is the corrected supply and return water temperature difference, βi is the dynamic influence coefficient (i=1,2,3,4), and Pe is the corrected supply and return water pressure difference;

[0067] Input Re and Pe into the hydraulic condition model to obtain the second corrected model data;

[0068] The correction module subtracts the first correction model data and the second correction model data from the corresponding output data to obtain a first difference and a second difference. If the first difference and the second difference are less than a preset difference threshold, an optimized hydraulic condition model is obtained. If the first difference and the second difference are not less than the preset difference threshold, the hydraulic condition model is corrected until it is less than the preset difference threshold, at which point the correction stops.

[0069] In some embodiments of this application, determining the target valve opening based on a prediction model and an optimized hydraulic condition model includes:

[0070] The prediction model is a time series prediction algorithm model, and the adjustment module is used to predict the supply and return water temperature difference and supply and return water pressure difference of multiple pipeline flow regulating valves according to the time series prediction algorithm model.

[0071] The predicted supply and return water temperature difference and supply and return water pressure difference are input into the optimized hydraulic condition model. With the goal of achieving flow balance within a preset time, the target valve opening of multiple pipeline flow regulating valves is output. The actual valve opening is adjusted to the target valve opening to obtain the actual flow value.

[0072] Determine whether the actual flow rate is within the preset flow rate range. If it is not within the preset flow rate range, determine whether the heating system is faulty. If the heating system is not faulty, adjust the actual valve opening until the actual flow rate is within the preset flow rate range. If the heating system is faulty, send an alarm signal.

[0073] In some embodiments of this application, the pressure transient rate of change when a heating system malfunctions is determined based on historical operating data, and the fault range and fault type are determined based on the pressure transient rate of change.

[0074] The system acquires pressure values ​​at multiple pipeline flow regulating valves and calculates the transient rate of pressure change. It then determines whether the transient rate of pressure change is within the fault range. If it is within the fault range, an alarm signal is sent, which includes the fault location and fault type.

[0075] This invention provides an adaptive identification and correction online hydraulic regulation method and system, which has the following advantages compared with the prior art:

[0076] This invention discloses an adaptive identification and correction online hydraulic regulation method and system. It establishes a hydraulic condition model with preset operating conditions, determines the correction method for the hydraulic condition model based on the preset operating conditions, and obtains a hydraulic condition model that most closely approximates the actual operating conditions of the heating network. It predicts the supply and return water temperature difference and pressure difference, inputs these predicted differences into the optimized hydraulic condition model, and outputs the target valve openings of multiple pipeline flow regulating valves with the goal of achieving flow balance within a preset time. The actual valve openings are then adjusted to the target valve openings, ensuring that the actual flow rate is within the preset flow range. This solves the problem that existing hydraulic condition models cannot closely approximate the actual operating conditions of the heating network, resulting in an imbalance between the actual heating flow rate and the design flow rate, leading to energy-consuming and time-consuming hydraulic regulation. Attached Figure Description

[0077] Figure 1 A flowchart illustrating an adaptive identification and correction online hydraulic regulation method according to an embodiment of the present invention is shown.

[0078] Figure 2 A schematic diagram of an adaptive identification and correction online hydraulic regulation system is shown in an embodiment of the present invention. Detailed Implementation

[0079] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0080] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0081] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0082] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0083] The following is a description of preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0084] like Figure 1 As shown, an embodiment of the present invention discloses an adaptive identification and correction online hydraulic regulation method, comprising:

[0085] Step S101: Establish a hydraulic condition model corresponding to the preset operating conditions of the heating system;

[0086] Step S102: Select the appropriate correction method according to the preset operating conditions, and optimize the hydraulic condition model according to the correction method;

[0087] Step S103: Establish a prediction model, determine the target valve opening based on the prediction model and the optimized hydraulic condition model, adjust the actual valve opening to the target valve opening, and determine whether the actual flow rate is within the preset flow range.

[0088] In this embodiment, the preset operating conditions include static hydraulic conditions and dynamic hydraulic conditions. The hydraulic condition model is modified by selecting the corresponding correction method according to the static hydraulic conditions and dynamic hydraulic conditions to obtain an optimized hydraulic condition model. The optimized hydraulic condition model is closer to the actual operating conditions of the heating network, so that the actual flow rate is kept within the preset flow rate range. The preset flow rate range is set according to the flow balance data of the heating network.

[0089] In some embodiments of this application, before selecting the corresponding correction method based on preset operating conditions, the method further includes:

[0090] Historical operating data is acquired, and the historical operating data is divided into input data and output data. The input data is then input into the corresponding hydraulic condition model to obtain the model data.

[0091] The historical operating data includes the supply water temperature, supply water pressure, valve opening, valve flow rate, return water temperature, and return water pressure of multiple pipeline flow regulating valves. The supply and return water temperature difference is determined based on the supply water temperature and return water temperature, and the supply and return water pressure difference is determined based on the supply water pressure and return water pressure.

[0092] The supply and return water temperature difference and supply and return water pressure difference are classified as input data, and the valve opening degree and valve flow rate are classified as output data.

[0093] In some embodiments of this application, a corresponding correction method is selected based on preset operating conditions, and the hydraulic condition model is optimized according to the correction method, including:

[0094] When the preset operating condition is static hydraulic condition:

[0095] Determine whether the difference between the model data and the output data is greater than a preset correction critical interval. If it is greater than the preset correction critical interval, correct the hydraulic condition model according to the first correction formula to obtain the first corrected model data. The model data includes the model valve opening degree and the model valve flow rate value. The difference between the model data and the output data includes the opening degree difference and the flow rate difference. The preset correction critical interval includes (opening degree correction critical value and flow rate correction critical value).

[0096] The first corrected formula is:

[0097]

[0098] Where T is the valve opening, T0 is the model valve opening, Tt is the opening correction critical value, Q is the valve flow rate, Q0 is the model valve flow rate, Qq is the flow rate correction critical value, and α is the hydraulic condition model loss rate.

[0099] When the preset operating condition is dynamic hydraulic condition:

[0100] The dynamic influence coefficient is determined according to the dynamic influence formula. The input data is then corrected according to the dynamic influence coefficient to obtain the corrected input data. The corrected input data is then input into the hydraulic condition model to obtain the second corrected model data.

[0101] The first preset input critical interval (R1, P1), the first preset input critical interval (R2, P2), and the third preset input critical interval (R3, P3) are determined according to the dynamic influence formula, and R1 < R2 < R3, P1 < P2 < P3; the corresponding dynamic influence parameters are selected according to the relationship between each preset input critical interval and the supply and return water temperature difference R0 and the supply and return water pressure difference P0.

[0102] When R0 < R1 and P0 < P1, the dynamic influence coefficient is β1 = k1 + n1 × (R0 + P0);

[0103] When R1≤R0<R2 and P1≤P0<P2, the dynamic influence coefficient is β2=k2+n2×(R0+P0);

[0104] When R2≤R0<R3 and P2≤P0<P3, the dynamic influence coefficient is β3=k3+n3×(R0+P0);

[0105] When R3≤R0 and P3≤P0, the dynamic influence coefficient is β4=βmax.

[0106] Where k1, k2, k3, n1, n2, n3 are all pre-defined constants;

[0107] The corrected input data is:

[0108]

[0109]

[0110] Where Re is the corrected supply and return water temperature difference, βi is the dynamic influence coefficient (i=1,2,3,4), and Pe is the corrected supply and return water pressure difference;

[0111] Input Re and Pe into the hydraulic condition model to obtain the second corrected model data;

[0112] The first and second modified model data are subtracted from the corresponding output data to obtain the first difference and the second difference. If the first difference and the second difference are less than the preset difference threshold, the optimized hydraulic condition model is obtained. If the first difference and the second difference are not less than the preset difference threshold, the hydraulic condition model is further modified until it is less than the preset difference threshold, at which point the modification stops.

[0113] In this embodiment, historical operating data shows that the same correction method is not suitable for hydraulic condition models under both static and dynamic hydraulic conditions. When under static hydraulic conditions, the difference between the model data and the output data determines whether the first correction formula is needed. The preset correction critical interval is the valve opening and flow rate when the flow balance is critical in static hydraulic conditions. When under dynamic hydraulic conditions, the dynamic influence coefficient needs to be considered. The corrected hydraulic condition model outputs the corrected model data, and the difference between the corrected model data and the output data is calculated again. If the difference is less than the preset difference threshold, the optimized hydraulic condition model is obtained, which greatly improves the accuracy of the hydraulic condition model.

[0114] In some embodiments of this application, a prediction model is established, and the target valve opening is determined based on the prediction model and the optimized hydraulic condition model, including:

[0115] The prediction model is a time series prediction algorithm model, which is used to predict the supply and return water temperature difference and supply and return water pressure difference of multiple pipeline flow regulating valves.

[0116] The predicted supply and return water temperature difference and supply and return water pressure difference are input into the optimized hydraulic condition model. With the goal of achieving flow balance within a preset time, the target valve opening of multiple pipeline flow regulating valves is output.

[0117] Adjust the actual valve opening to the target valve opening to obtain the actual flow rate value;

[0118] Determine whether the actual flow rate is within the preset flow rate range. If it is not within the preset flow rate range, determine whether the heating system is faulty. If the heating system is not faulty, adjust the actual valve opening until the actual flow rate is within the preset flow rate range. If the heating system is faulty, send an alarm signal.

[0119] In some embodiments of this application, determining whether the heating system is faulty includes:

[0120] Determine the transient pressure change rate when the heating system malfunctions based on historical operating data, and determine the fault range and fault type based on the transient pressure change rate;

[0121] The system acquires pressure values ​​at multiple pipeline flow regulating valves and calculates the transient rate of pressure change. It then determines whether the transient rate of pressure change is within the fault range. If it is within the fault range, an alarm signal is sent, which includes the fault location and fault type.

[0122] In this embodiment, the transient pressure change rate when the heating system malfunctions specifically refers to the transient pressure change when abnormal operations such as sudden power failure of the circulating pump or relay pump, abnormal closure of the main valve of the transmission line, or cessation of heating by the heat source heat exchanger occur. By identifying problems and faults in the heating network, timely repairs can be carried out, thereby improving the heating quality and reducing operating energy consumption while ensuring heating safety.

[0123] In some embodiments of this application, such as Figure 2 As shown, it also includes an adaptive identification and correction online hydraulic regulation system:

[0124] A module is established to create a hydraulic condition model corresponding to the preset operating conditions of the heating system.

[0125] The correction module is used to select the appropriate correction method according to the preset operating conditions and optimize the hydraulic condition model according to the correction method.

[0126] The adjustment module is used to establish a prediction model, determine the target valve opening based on the prediction model and the optimized hydraulic condition model, adjust the actual valve opening to the target valve opening, and determine whether the actual flow rate is within the preset flow range.

[0127] In some embodiments of this application, before selecting the corresponding correction method based on preset operating conditions, the method further includes:

[0128] Historical operating data is acquired, and the historical operating data is divided into input data and output data. The input data is then input into the corresponding hydraulic condition model to obtain the model data.

[0129] The historical operating data includes the supply water temperature, supply water pressure, valve opening, valve flow rate, return water temperature, and return water pressure of multiple pipeline flow regulating valves. The supply and return water temperature difference is determined based on the supply water temperature and return water temperature, and the supply and return water pressure difference is determined based on the supply water pressure and return water pressure.

[0130] The supply and return water temperature difference and supply and return water pressure difference are classified as input data, and the valve opening degree and valve flow rate are classified as output data.

[0131] In some embodiments of this application, a corresponding correction method is selected based on preset operating conditions, and the hydraulic condition model is optimized according to the correction method, including:

[0132] When the preset operating condition is static hydraulic condition:

[0133] The correction module is used to determine whether the difference between the model data and the output data is greater than a preset correction critical interval. If it is greater than the preset correction critical interval, the hydraulic condition model is corrected according to the first correction formula to obtain the first corrected model data. The model data includes the model valve opening degree and the model valve flow rate value. The difference between the model data and the output data includes the opening degree difference and the flow rate difference. The preset correction critical interval includes (opening degree correction critical value, flow rate correction critical value).

[0134] The first corrected formula is:

[0135]

[0136] Where T is the valve opening, T0 is the model valve opening, Tt is the opening correction critical value, Q is the valve flow rate, Q0 is the model valve flow rate, Qq is the flow rate correction critical value, and α is the hydraulic condition model loss rate.

[0137] When the preset operating condition is dynamic hydraulic condition:

[0138] The correction module is used to determine the dynamic influence coefficient according to the dynamic influence formula, correct the input data according to the dynamic influence coefficient, obtain the corrected input data, and input the corrected input data into the hydraulic condition model to obtain the second corrected model data.

[0139] The correction module is also used to determine the first preset input critical interval (R1, P1), the first preset input critical interval (R2, P2), and the third preset input critical interval (R3, P3) according to the dynamic influence formula, where R1 < R2 < R3 and P1 < P2 < P3; and to select the corresponding dynamic influence parameters according to the relationship between each preset input critical interval and the supply and return water temperature difference R0 and the supply and return water pressure difference P0.

[0140] When R0 < R1 and P0 < P1, the dynamic influence coefficient is β1 = k1 + n1 × (R0 + P0);

[0141] When R1≤R0<R2 and P1≤P0<P2, the dynamic influence coefficient is β2=k2+n2×(R0+P0);

[0142] When R2≤R0<R3 and P2≤P0<P3, the dynamic influence coefficient is β3=k3+n3×(R0+P0);

[0143] When R3≤R0 and P3≤P0, the dynamic influence coefficient is β4=βmax.

[0144] Where k1, k2, k3, n1, n2, n3 are all pre-defined constants;

[0145] The corrected input data is:

[0146]

[0147]

[0148] Where Re is the corrected supply and return water temperature difference, βi is the dynamic influence coefficient (i=1,2,3,4), and Pe is the corrected supply and return water pressure difference;

[0149] Input Re and Pe into the hydraulic condition model to obtain the second corrected model data;

[0150] The correction module subtracts the first correction model data and the second correction model data from the corresponding output data to obtain a first difference and a second difference. If the first difference and the second difference are less than a preset difference threshold, an optimized hydraulic condition model is obtained. If the first difference and the second difference are not less than the preset difference threshold, the hydraulic condition model is corrected until it is less than the preset difference threshold, at which point the correction stops.

[0151] In some embodiments of this application, determining the target valve opening based on a prediction model and an optimized hydraulic condition model includes:

[0152] The prediction model is a time series prediction algorithm model, and the adjustment module is used to predict the supply and return water temperature difference and supply and return water pressure difference of multiple pipeline flow regulating valves according to the time series prediction algorithm model.

[0153] The predicted supply and return water temperature difference and supply and return water pressure difference are input into the optimized hydraulic condition model. With the goal of achieving flow balance within a preset time, the target valve opening of multiple pipeline flow regulating valves is output. The actual valve opening is adjusted to the target valve opening to obtain the actual flow value.

[0154] Determine whether the actual flow rate is within the preset flow rate range. If it is not within the preset flow rate range, determine whether the heating system is faulty. If the heating system is not faulty, adjust the actual valve opening until the actual flow rate is within the preset flow rate range. If the heating system is faulty, send an alarm signal.

[0155] In some embodiments of this application, the pressure transient rate of change when a heating system malfunctions is determined based on historical operating data, and the fault range and fault type are determined based on the pressure transient rate of change.

[0156] The system acquires pressure values ​​at multiple pipeline flow regulating valves and calculates the transient rate of pressure change. It then determines whether the transient rate of pressure change is within the fault range. If it is within the fault range, an alarm signal is sent, which includes the fault location and fault type.

[0157] In summary, this invention discloses an adaptive identification and correction online hydraulic regulation method and system. The method includes: step S101: establishing a hydraulic condition model corresponding to the preset operating conditions of the heating system; step S102: selecting a corresponding correction method according to the preset operating conditions, and optimizing the hydraulic condition model according to the correction method; step S103: establishing a prediction model, determining the target valve opening degree according to the prediction model and the optimized hydraulic condition model, adjusting the actual valve opening degree to the target valve opening degree, and determining whether the actual flow rate value is within the preset flow range.

[0158] This invention establishes a hydraulic condition model with preset operating conditions. By setting historical operating data as input and output data, the input data is fed into the hydraulic condition model to obtain model data. Based on the output data and model data, the hydraulic condition model is corrected to obtain a hydraulic condition model that most closely approximates the actual operating conditions of the heating network. The model predicts the supply and return water temperature difference and pressure difference, and inputs the predicted supply and return water temperature difference and pressure difference into the optimized hydraulic condition model. With the goal of achieving flow balance within a preset time, the model outputs the target valve opening of multiple pipeline flow regulating valves. The actual valve opening is adjusted to the target valve opening, so that the actual flow value is within the preset flow range. This solves the problem that existing hydraulic condition models cannot closely approximate the actual operating conditions of the heating network, and the imbalance between the actual heating flow and the design flow leads to energy-consuming and time-consuming hydraulic regulation.

[0159] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0160] Although the invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, features in the embodiments disclosed herein can be combined with each other in any manner, provided there is no structural conflict. The omission of all such combinations in this specification is merely for brevity and resource conservation. Therefore, the invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

[0161] It will be understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An adaptive identification and correction method for online hydraulic regulation, characterized in that, include: Establish a hydraulic condition model corresponding to the preset operating conditions of the heating system; Select the appropriate correction method based on the preset operating conditions, and optimize the hydraulic condition model according to the correction method; Establish a prediction model, determine the target valve opening based on the prediction model and the optimized hydraulic condition model, adjust the actual valve opening to the target valve opening, and determine whether the actual flow rate is within the preset flow range. Based on the preset operating conditions, a corresponding correction method is selected, and the hydraulic condition model is optimized according to the correction method, including: When the preset operating condition is static hydraulic condition: Determine whether the difference between the model data and the output data is greater than a preset correction critical interval. If it is greater than the preset correction critical interval, correct the hydraulic condition model according to the first correction formula to obtain the first corrected model data. The model data includes the model valve opening degree and the model valve flow rate value. The difference between the model data and the output data includes the opening degree difference and the flow rate difference. The preset correction critical interval includes (opening degree correction critical value and flow rate correction critical value). The first corrected formula is: ； Where T is the valve opening, T0 is the model valve opening, Tt is the opening correction critical value, Q is the valve flow rate, Q0 is the model valve flow rate, Qq is the flow rate correction critical value, and α is the hydraulic condition model loss rate. When the preset operating condition is dynamic hydraulic condition: The dynamic influence coefficient is determined according to the dynamic influence formula. The input data is then corrected according to the dynamic influence coefficient to obtain the corrected input data. The corrected input data is then input into the hydraulic condition model to obtain the second corrected model data. The first preset input critical interval (R1, P1), the first preset input critical interval (R2, P2), and the third preset input critical interval (R3, P3) are determined according to the dynamic influence formula, and R1 < R2 < R3, P1 < P2 < P3; the corresponding dynamic influence coefficients are selected according to the relationship between each preset input critical interval and the supply and return water temperature difference R0 and the supply and return water pressure difference P0. When R0 < R1 and P0 < P1, the dynamic influence coefficient is β1 = k1 + n1 × (R0 + P0). When R1≤R0<R2 and P1≤P0<P2, the dynamic influence coefficient is β2=k2+n2×(R0+P0). When R2≤R0<R3 and P2≤P0<P3, the dynamic influence coefficient is β3=k3+n3×(R0+P0). When R3≤R0 and P3≤P0, the dynamic influence coefficient is β4=βmax; Where k1, k2, k3, n1, n2, n3 are all pre-defined constants; The corrected input data is: ； ； Where Re is the corrected supply and return water temperature difference, βi is the dynamic influence coefficient (i=1,2,3,4), and Pe is the corrected supply and return water pressure difference; Input Re and Pe into the hydraulic condition model to obtain the second corrected model data; The first and second modified model data are subtracted from the corresponding output data to obtain the first difference and the second difference. If the first difference and the second difference are less than the preset difference threshold, the optimized hydraulic condition model is obtained. If the first difference and the second difference are not less than the preset difference threshold, the hydraulic condition model is further modified until it is less than the preset difference threshold, at which point the modification stops.

2. The adaptive identification and correction online hydraulic regulation method according to claim 1, characterized in that, Before selecting the appropriate correction method based on the preset operating conditions, the following steps are also included: Historical operating data is acquired, and the historical operating data is divided into input data and output data. The input data is then input into the corresponding hydraulic condition model to obtain the model data. The historical operating data includes the supply water temperature, supply water pressure, valve opening, valve flow rate, return water temperature, and return water pressure of multiple pipeline flow regulating valves. The supply and return water temperature difference is determined based on the supply water temperature and return water temperature, and the supply and return water pressure difference is determined based on the supply water pressure and return water pressure. The supply and return water temperature difference and supply and return water pressure difference are classified as input data, and the valve opening degree and valve flow rate are classified as output data.

3. The adaptive identification and correction online hydraulic regulation method according to claim 1, characterized in that, Establish a predictive model, and determine the target valve opening based on the predictive model and the optimized hydraulic condition model, including: The prediction model is a time series prediction algorithm model, which is used to predict the supply and return water temperature difference and supply and return water pressure difference of multiple pipeline flow regulating valves. The predicted supply and return water temperature difference and supply and return water pressure difference are input into the optimized hydraulic condition model. With the goal of achieving flow balance within a preset time, the target valve opening of multiple pipeline flow regulating valves is output. Adjust the actual valve opening to the target valve opening to obtain the actual flow rate value; Determine whether the actual flow rate is within the preset flow rate range. If it is not within the preset flow rate range, determine whether the heating system is faulty. If the heating system is not faulty, adjust the actual valve opening until the actual flow rate is within the preset flow rate range. If the heating system is faulty, send an alarm signal.

4. The adaptive identification and correction online hydraulic regulation method according to claim 3, characterized in that, Determining whether a heating system is malfunctioning includes: Determine the transient pressure change rate when the heating system malfunctions based on historical operating data, and determine the fault range and fault type based on the transient pressure change rate; The system acquires pressure values ​​at multiple pipeline flow regulating valves and calculates the transient rate of pressure change. It then determines whether the transient rate of pressure change is within the fault range. If it is within the fault range, an alarm signal is sent, which includes the fault location and fault type.

5. An adaptive identification and correction online hydraulic regulation system, characterized in that, include: A module is established to create a hydraulic condition model corresponding to the preset operating conditions of the heating system. The correction module is used to select the appropriate correction method according to the preset operating conditions and optimize the hydraulic condition model according to the correction method. The adjustment module is used to establish a prediction model, determine the target valve opening based on the prediction model and the optimized hydraulic condition model, adjust the actual valve opening to the target valve opening, and determine whether the actual flow value is within the preset flow range. Based on the preset operating conditions, a corresponding correction method is selected, and the hydraulic condition model is optimized according to the correction method, including: When the preset operating condition is static hydraulic condition: Determine whether the difference between the model data and the output data is greater than a preset correction critical interval. If it is greater than the preset correction critical interval, correct the hydraulic condition model according to the first correction formula to obtain the first corrected model data. The model data includes the model valve opening degree and the model valve flow rate value. The difference between the model data and the output data includes the opening degree difference and the flow rate difference. The preset correction critical interval includes (opening degree correction critical value and flow rate correction critical value). The first corrected formula is: ； Where T is the valve opening, T0 is the model valve opening, Tt is the opening correction critical value, Q is the valve flow rate, Q0 is the model valve flow rate, Qq is the flow rate correction critical value, and α is the hydraulic condition model loss rate. When the preset operating condition is dynamic hydraulic condition: The dynamic influence coefficient is determined according to the dynamic influence formula. The input data is then corrected according to the dynamic influence coefficient to obtain the corrected input data. The corrected input data is then input into the hydraulic condition model to obtain the second corrected model data. The first preset input critical interval (R1, P1), the first preset input critical interval (R2, P2), and the third preset input critical interval (R3, P3) are determined according to the dynamic influence formula, and R1 < R2 < R3, P1 < P2 < P3; the corresponding dynamic influence parameters are selected according to the relationship between each preset input critical interval and the supply and return water temperature difference R0 and the supply and return water pressure difference P0. When R0 < R1 and P0 < P1, the dynamic influence coefficient is β1 = k1 + n1 × (R0 + P0). When R1≤R0<R2 and P1≤P0<P2, the dynamic influence coefficient is β2=k2+n2×(R0+P0). When R2≤R0<R3 and P2≤P0<P3, the dynamic influence coefficient is β3=k3+n3×(R0+P0). When R3≤R0 and P3≤P0, the dynamic influence coefficient is β4=βmax; Where k1, k2, k3, n1, n2, n3 are all pre-defined constants; The corrected input data is: ； ； Where Re is the corrected supply and return water temperature difference, βi is the dynamic influence coefficient (i=1,2,3,4), and Pe is the corrected supply and return water pressure difference; Input Re and Pe into the hydraulic condition model to obtain the second corrected model data; The first and second modified model data are subtracted from the corresponding output data to obtain the first difference and the second difference. If the first difference and the second difference are less than the preset difference threshold, the optimized hydraulic condition model is obtained. If the first difference and the second difference are not less than the preset difference threshold, the hydraulic condition model is further modified until it is less than the preset difference threshold, at which point the modification stops.

6. The adaptive identification and correction online hydraulic regulation system according to claim 5, characterized in that, Before selecting the appropriate correction method based on the preset operating conditions, the following steps are also included: Historical operating data is acquired, and the historical operating data is divided into input data and output data. The input data is then input into the corresponding hydraulic condition model to obtain the model data. The historical operating data includes the supply water temperature, supply water pressure, valve opening, valve flow rate, return water temperature, and return water pressure of multiple pipeline flow regulating valves. The supply and return water temperature difference is determined based on the supply water temperature and return water temperature, and the supply and return water pressure difference is determined based on the supply water pressure and return water pressure. The supply and return water temperature difference and supply and return water pressure difference are classified as input data, and the valve opening degree and valve flow rate are classified as output data.

7. The adaptive identification and correction online hydraulic regulation system according to claim 6, characterized in that, The target valve opening is determined based on the prediction model and the optimized hydraulic condition model, including: The prediction model is a time series prediction algorithm model, and the adjustment module is used to predict the supply and return water temperature difference and supply and return water pressure difference of multiple pipeline flow regulating valves according to the time series prediction algorithm model. The predicted supply and return water temperature difference and supply and return water pressure difference are input into the optimized hydraulic condition model. With the goal of achieving flow balance within a preset time, the target valve opening of multiple pipeline flow regulating valves is output. The actual valve opening is adjusted to the target valve opening to obtain the actual flow value. Determine whether the actual flow rate is within the preset flow rate range. If it is not within the preset flow rate range, determine whether the heating system is faulty. If the heating system is not faulty, adjust the actual valve opening until the actual flow rate is within the preset flow rate range. If the heating system is faulty, send an alarm signal.

8. The adaptive identification and correction online hydraulic regulation system according to claim 7, characterized in that, Determine the transient pressure change rate when the heating system malfunctions based on historical operating data, and determine the fault range and fault type based on the transient pressure change rate; The system acquires pressure values ​​at multiple pipeline flow regulating valves and calculates the transient rate of pressure change. It then determines whether the transient rate of pressure change is within the fault range. If it is within the fault range, an alarm signal is sent, which includes the fault location and fault type.

Images