Heating system water attack risk simulation analysis method

Abstract

The invention relates to the technical field of heat supply systems, in particular to a water attack risk simulation analysis method for a heat supply system. Comprising the steps of establishing an initial simulation model of the heat supply system; generating a plurality of simulation sub-models according to the initial simulation model and the simulation operation resource parameters, and sequentially setting simulation parameters of the simulation sub-models; and generating a primary simulation result of each simulation sub-model, and generating optimization parameters of the heat supply system according to all primary simulation results. According to the method, the initial simulation model is constructed according to the equipment parameters of the heat supply system, and the initial simulation model is split according to the complexity and the simulation operation resource quantity of the heat supply system, so that the accuracy and the calculation efficiency of an analogue simulation result are improved. And the water attack risk in the operation process of the heat supply system is reduced, so that the operation stability and safety of the heat supply system are improved.

Description

Technical Field

[0001] The present application relates to the technical field of heating systems, and in particular to a method for simulating and analyzing water hammer risks in heating systems. Background Art

[0002] In the heating system, water hammer can affect the safe operation of the system. The large pressure fluctuations caused by water hammer will cause the system to experience instantaneous pressure increases and decreases. When the pressure in the pipe network increases instantly, the equipment, pipes and pipe accessories may be damaged due to insufficient pressure bearing capacity. In addition, when the pressure in the pipe network drops instantly, the high-temperature hot water transported by the thermal network may vaporize to form cavitation, destroying the continuity of the water flow. When the cavitation in the pipe network encounters the rising pressure wave, the cavitation collapses, forming a new pressure transient process, which will affect the safe operation of the heating system.

[0003] Water hammer is most likely to occur during the process of stopping and restarting the pump, power outages due to accidents, or improper opening and closing of valves. Especially for pipe networks with high medium temperature, long conveying distance, and large drop, measures should be taken to prevent water hammer and minimize the damage caused by water hammer. Summary of the invention

[0004] The purpose of this application is: to solve the above-mentioned technical problems, this application provides a water hammer risk simulation and analysis method for a heating system, aiming to evaluate the feasibility of different optimization schemes through simulation and improve the stability and safety of the operation of the heating system.

[0005] In some embodiments of the present application, an initial simulation model is constructed according to equipment parameters of the heating system, and the initial simulation model is split according to the complexity of the heating system and the amount of simulation computing resources, thereby improving the accuracy and computational efficiency of the simulation results.

[0006] In some embodiments of the present application, multiple optimization plans are formulated based on the simulation results of the heating system, and each optimization plan is simulated to evaluate the feasibility of different optimization plans (such as pipe diameter adjustment, valve type selection, pump performance optimization, etc.), and optimization parameters are formulated based on the evaluation results to reduce the risk of water hammer during the operation of the heating system, thereby improving the stability and safety of the operation of the heating system.

[0007] In some embodiments of the present application, a method for simulating and analyzing water hammer risk in a heating system is provided, comprising:

[0008] Establish an initial simulation model of the heating system;

[0009] Generate multiple simulation sub-models according to the initial simulation model and simulation operation resource parameters, and set simulation parameters of each simulation sub-model in turn;

[0010] Generate the first-level simulation results of each simulation sub-model, and generate the optimization parameters of the heating system based on all the first-level simulation results.

[0011] In some embodiments of the present application, when establishing an initial simulation model, the steps include:

[0012] Obtain all equipment parameters of the heating system;

[0013] Establish the initial geometric model;

[0014] Obtain material properties of each device and generate material characteristic data packets;

[0015] Set all boundary conditions and generate boundary condition data packages;

[0016] Import material characteristics data package and boundary condition data package into the initial geometry model;

[0017] Establish an initial simulation model based on the imported results.

[0018] In some embodiments of the present application, when generating multiple simulation sub-models, it includes:

[0019] Generate a first reference value H1 according to a preset evaluation model;

[0020]

[0021] Among them, ai is the reference value of the i-th characteristic index in the initial simulation model; αi is the influencing factor of the i-th characteristic index; r is the number of characteristic indicators;

[0022] Generate a second reference evaluation value H2 according to the simulation operation resource parameter;

[0023] Generate a simulation evaluation value b according to the first reference evaluation value H1 and the second reference evaluation value H2;

[0024] b=μ1*H1+μ2*H2;

[0025] Wherein, μ1 is the preset first weight coefficient; μ2 is the preset second weight coefficient;

[0026] The number n of simulation sub-models is set according to the simulation evaluation value b;

[0027] Generate multiple simulation sub-models according to the number n of simulation sub-models;

[0028] A simulation sub-model sequence P is established, where P = (p1, p2...pi...pn), wherein pi is the ith simulation sub-model.

[0029] In some embodiments of the present application, when setting the number of simulation sub-models n, it includes:

[0030] Preset a first simulation evaluation value interval (B1, B2), preset a second simulation evaluation value interval (B2, B3) and a third simulation evaluation value interval (B3, B4);

[0031] If the simulation evaluation value b is within the preset first simulation evaluation value interval, the number of simulation sub-models n is set to the preset first number value n1, that is, n=n1;

[0032] If the simulation evaluation value b is within the preset second simulation evaluation value interval, the number of simulation sub-models n is set to the preset second number value n2, that is, n=n2;

[0033] If the simulation evaluation value b is within the preset third simulation evaluation value interval, the number n of simulation sub-models is set to the preset third number value n3, that is, n=n3, and n1<n2<n3.

[0034] In some embodiments of the present application, when a plurality of simulation sub-models are generated according to the number n of simulation sub-models, the method includes:

[0035] The initial simulation model is segmented according to the number n of simulation sub-models, and a plurality of segmentation plans are generated;

[0036] Establish a segmentation plan sequence C, C = (c1, c2...ci...cm), where ci is the i-th segmentation plan and m is the number of segmentation plans;

[0037] Any single partitioning plan in the partitioning plan sequence C includes n simulation sub-models;

[0038] Generate the operation evaluation value of each segmentation plan in turn;

[0039] Establish an operation evaluation value sequence D, D = (d1, d2...di...dm), where di is the operation evaluation value of the i-th segmentation plan;

[0040] Set the segmentation plan corresponding to the maximum value dmax in the running evaluation value sequence D as the first-level plan;

[0041] A simulation sub-model sequence P is established according to the first-level plan.

[0042] In some embodiments of the present application, when the operation evaluation value of each segmentation plan is generated in sequence, it includes:

[0043] Sequentially select the i-th segmentation plan as the target segmentation plan;

[0044] Generate an operational evaluation value d of the target segmentation plan;

[0045]

[0046] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second weight coefficient; Q3 is the preset third fixed coefficient; ki is the model evaluation value of the i-th simulation sub-model in the target segmentation plan; ji is the impact evaluation value between the i-th simulation sub-model and the i+1-th simulation sub-model; U is the reference value for the total simulation operation time and operation load parameter setting based on the target segmentation plan.

[0047] In some embodiments of the present application, when setting the simulation parameters of each simulation sub-model in sequence, it includes:

[0048] According to the simulation sub-model sequence P, pi is set as the target simulation sub-model in sequence;

[0049] Generate a model evaluation value k of the target simulation sub-model;

[0050]

[0051] Among them, gi is the reference value of the i-th model evaluation index in the target simulation sub-model; βi is the influencing factor of the i-th model evaluation index; r1 is the number of model evaluation indicators;

[0052] According to the model evaluation value, the simulation operation resource ratio and simulation operation time of the target simulation sub-model are set;

[0053] Set the working condition simulation parameters of the target simulation sub-model;

[0054] Generates the simulation operation sequence for each simulation sub-model.

[0055] In some embodiments of the present application, when generating the primary simulation results of each simulation sub-model, it includes:

[0056] Create a relationship table;

[0057] Output the initial simulation results of each simulation sub-model in sequence according to the simulation operation order;

[0058] Obtain the associated sub-model of the target simulation sub-model according to the association relationship table;

[0059] Correcting the initial simulation result of the target simulation sub-model according to the initial simulation results of all associated sub-models, and generating the first-level simulation result of the target simulation sub-model;

[0060] Generate the first-level simulation results of each target simulation sub-model in turn.

[0061] In some embodiments of the present application, when generating the optimization parameters of the heating system according to all the first-level simulation results, it includes:

[0062] Generate multiple risk nodes based on all first-level simulation results;

[0063] Establish multiple first-level optimization plans based on all risk nodes;

[0064] Simulate each first-level optimization plan in turn, and generate an optimization evaluation value of each first-level optimization plan according to the simulation results;

[0065] Establish the optimized evaluation value sequence F, F = (f1, f2…f i …f m1 ), where f i is the optimization evaluation value of the i-th first-level optimization plan; m1 is the number of first-level optimization plans;

[0066] Set the first-level optimization plan corresponding to the maximum value fi in the optimization evaluation value sequence F as the second-level optimization plan;

[0067] Generate the optimization parameters of the heating system according to the secondary optimization plan.

[0068] Compared with the prior art, the method for simulating and analyzing water hammer risk in a heating system according to the embodiment of the present application has the following beneficial effects:

[0069] An initial simulation model is constructed according to the equipment parameters of the heating system, and is split according to the complexity of the heating system and the amount of simulation computing resources, thereby improving the accuracy and computational efficiency of the simulation results.

[0070] According to the simulation results of the heating system, a variety of optimization plans are formulated, and each optimization plan is simulated to evaluate the feasibility of different optimization plans (such as pipe diameter adjustment, valve type selection, pump performance optimization, etc.). According to the evaluation results, optimization parameters are formulated to reduce the risk of water hammer during the operation of the heating system, thereby improving the stability and safety of the heating system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a flow chart of a method for simulating and analyzing water hammer risk in a heating system in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0072] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0073] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0074] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0075] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0076] like Figure 1 As shown, a method for simulating and analyzing water hammer risk in a heating system according to a preferred embodiment of the present application includes:

[0077] S101: Establishing an initial simulation model of the heating system;

[0078] S102: generating a plurality of simulation sub-models according to the initial simulation model and the simulation operation resource parameters, and setting the simulation parameters of each simulation sub-model in turn;

[0079] S103: Generate the first-level simulation results of each simulation sub-model, and generate the optimization parameters of the heating system according to all the first-level simulation results.

[0080] Specifically, when establishing the initial simulation model, it includes:

[0081] Obtain all equipment parameters of the heating system;

[0082] Establish the initial geometric model;

[0083] Obtain material properties of each device and generate material characteristic data packets;

[0084] Set all boundary conditions and generate boundary condition data packages;

[0085] Import material characteristics data package and boundary condition data package into the initial geometry model;

[0086] Establish an initial simulation model based on the imported results.

[0087] Specifically, an initial three-dimensional collective model is established based on the equipment parameters of the heating system, and its equipment includes: pipes, valves, pumps, heat exchangers and other equipment.

[0088] Specifically, input the physical properties of equipment such as pipes, valves, pumps, etc., such as the elastic modulus, density, thermal conductivity, etc. of the material. Define the boundary conditions of the system, such as inlet pressure, temperature, flow, etc., as well as the outlet conditions. Set the initial state of the simulation, such as the state of the system at startup or the state when the valve is closed. Thus, the initial simulation model is constructed.

[0089] Specifically, all areas of the heating system are included in the initial simulation model.

[0090] In a preferred embodiment of the present application, when generating multiple simulation sub-models, it includes:

[0091] Generate a first reference value H1 according to a preset evaluation model;

[0092]

[0093] Among them, ai is the reference value of the i-th characteristic index in the initial simulation model; αi is the influencing factor of the i-th characteristic index; r is the number of characteristic indicators;

[0094] Generate a second reference evaluation value H2 according to the simulation operation resource parameter;

[0095] Generate a simulation evaluation value b according to the first reference evaluation value H1 and the second reference evaluation value H2;

[0096] b=μ1*H1+μ2*H2;

[0097] Wherein, μ1 is the preset first weight coefficient; μ2 is the preset second weight coefficient;

[0098] The number n of simulation sub-models is set according to the simulation evaluation value b;

[0099] Generate multiple simulation sub-models according to the number n of simulation sub-models;

[0100] A simulation sub-model sequence P is established, where P = (p1, p2...pi...pn), wherein pi is the ith simulation sub-model.

[0101] Specifically, the first reference evaluation value and the second reference evaluation value have the same value range. The higher the complexity of the heating system, the larger the corresponding first reference evaluation value. The fewer the simulation operation resources, the larger the corresponding second reference evaluation value.

[0102] Specifically, characteristic indicators include but are not limited to the number of equipment in the initial simulation model, pipeline length, pipeline bending nodes, average water flow rate and other parameters that affect water hammer risk.

[0103] Specifically, the larger the simulation evaluation value is, the lower the efficiency of the simulation calculation of the current heating system is.

[0104] Specifically, a single simulation sub-model includes a partial area of ​​the heating system, which can perform local simulation analysis on the heating system, and all simulation sub-models in the simulation sub-model array P can complete the complete analysis of the heating system.

[0105] Specifically, when setting the number of simulation sub-models n, it includes:

[0106] Preset a first simulation evaluation value interval (B1, B2), preset a second simulation evaluation value interval (B2, B3) and a third simulation evaluation value interval (B3, B4);

[0107] If the simulation evaluation value b is within the preset first simulation evaluation value interval, the number of simulation sub-models n is set to the preset first number value n1, that is, n=n1;

[0108] If the simulation evaluation value b is within the preset second simulation evaluation value interval, the number of simulation sub-models n is set to the preset second number value n2, that is, n=n2;

[0109] If the simulation evaluation value b is within the preset third simulation evaluation value interval, the number n of simulation sub-models is set to the preset third number value n3, that is, n=n3, and n1<n2<n3.

[0110] It can be understood that in the above embodiment, the number of simulation sub-models is dynamically adjusted according to the real-time simulation evaluation value, so that the constructed initial simulation model is divided to generate multiple simulation sub-models, and simulation analysis is performed on each simulation sub-model separately, thereby improving the accuracy and efficiency of the simulation analysis of the heating system.

[0111] In a preferred embodiment of the present application, when generating multiple simulation sub-models according to the number of simulation sub-models n, it includes:

[0112] The initial simulation model is segmented according to the number n of simulation sub-models, and a plurality of segmentation plans are generated;

[0113] Establish a segmentation plan sequence C, C = (c1, c2...ci...cm), where ci is the i-th segmentation plan and m is the number of segmentation plans;

[0114] Any single partitioning plan in the partitioning plan sequence C includes n simulation sub-models;

[0115] Generate the operation evaluation value of each segmentation plan in turn;

[0116] Establish an operation evaluation value sequence D, D = (d1, d2...di...dm), where di is the operation evaluation value of the i-th segmentation plan;

[0117] Set the segmentation plan corresponding to the maximum value dmax in the running evaluation value sequence D as the first-level plan;

[0118] A simulation sub-model sequence P is established according to the first-level plan.

[0119] Specifically, multiple segmentation plans are generated according to the equipment nodes in the heating system, and all simulation sub-models within a single segmentation plan can perform a comprehensive simulation and analysis of the heating system.

[0120] Specifically, the areas mapped by the ith simulation sub-model and the i+1th simulation sub-model in a single segmentation plan are adjacent, and the water flow direction is from the area corresponding to the ith simulation sub-model to the area corresponding to the i+1th simulation sub-model.

[0121] Specifically, the higher the operation evaluation value, the higher the simulation analysis efficiency and analysis accuracy of all simulation sub-models in the current segmentation plan.

[0122] Specifically, when generating the operation evaluation values ​​of each segmentation plan in sequence, it includes:

[0123] Sequentially select the i-th segmentation plan as the target segmentation plan;

[0124] Generate an operational evaluation value d of the target segmentation plan;

[0125]

[0126] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second weight coefficient; Q3 is the preset third fixed coefficient; ki is the model evaluation value of the i-th simulation sub-model in the target segmentation plan; ji is the impact evaluation value between the i-th simulation sub-model and the i+1-th simulation sub-model; U is the reference value for the total simulation operation time and operation load parameter setting based on the target segmentation plan.

[0127] Specifically, the impact evaluation value is set based on the correlation between the regions mapped by the two simulation sub-models. The higher the correlation, the higher the corresponding impact evaluation value.

[0128] Specifically, the total simulation time is the time it takes for all simulation sub-models in a single segmentation task to complete the simulation. The smaller the total simulation operation time, the larger the corresponding reference value U. The operation load parameter refers to the average and peak resource usage during simulation analysis. The larger the load, the smaller the corresponding reference value U.

[0129] It can be understood that in the above embodiment, the initial simulation model is constructed according to the equipment parameters of the heating system, and the initial simulation model is split according to the complexity of the heating system and the amount of simulation computing resources, so as to improve the accuracy and calculation efficiency of the simulation results.

[0130] In a preferred embodiment of the present application, when setting the simulation parameters of each simulation sub-model in sequence, it includes:

[0131] According to the simulation sub-model sequence P, pi is set as the target simulation sub-model in sequence;

[0132] Generate a model evaluation value k of the target simulation sub-model;

[0133]

[0134] Among them, gi is the reference value of the i-th model evaluation index in the target simulation sub-model; βi is the influencing factor of the i-th model evaluation index; r1 is the number of model evaluation indicators;

[0135] According to the model evaluation value, the simulation operation resource ratio and simulation operation time of the target simulation sub-model are set;

[0136] Set the working condition simulation parameters of the target simulation sub-model;

[0137] Generates the simulation operation sequence for each simulation sub-model.

[0138] Specifically, the model evaluation indicators include, but are not limited to, parameters such as the number of equipment in the area corresponding to the simulation sub-model, water flow intensity, and the number of adjacent simulation sub-models.

[0139] Specifically, when setting the operating simulation parameters, the main objectives of the simulation need to be clearly defined, such as predicting the peak pressure of the water flow, assessing the risk of pipeline damage, etc.

[0140] Specifically, when generating the first-level simulation results of each simulation sub-model, it includes:

[0141] Create a relationship table;

[0142] Output the initial simulation results of each simulation sub-model in sequence according to the simulation operation order;

[0143] Obtain the associated sub-model of the target simulation sub-model according to the association relationship table;

[0144] Correcting the initial simulation result of the target simulation sub-model according to the initial simulation results of all associated sub-models, and generating the first-level simulation result of the target simulation sub-model;

[0145] Generate the first-level simulation results of each target simulation sub-model in turn.

[0146] Specifically, it is determined whether the corresponding regions of each simulation sub-model are adjacent to each other. If so, the corresponding two simulation sub-models are mutually associated sub-models, and an associated relationship table is generated based on all associated relationships.

[0147] In a preferred embodiment of the present application, when generating the optimization parameters of the heating system according to all the first-level simulation results, it includes:

[0148] Generate multiple risk nodes based on all first-level simulation results;

[0149] Establish multiple first-level optimization plans based on all risk nodes;

[0150] Simulate each first-level optimization plan in turn, and generate an optimization evaluation value of each first-level optimization plan according to the simulation results;

[0151] Establish the optimized evaluation value sequence F, F = (f1, f2…f i …f m1 ), where f i is the optimization evaluation value of the i-th first-level optimization plan; m1 is the number of first-level optimization plans;

[0152] Set the first-level optimization plan corresponding to the maximum value fi in the optimization evaluation value sequence F as the second-level optimization plan;

[0153] Generate the optimization parameters of the heating system according to the secondary optimization plan.

[0154] Specifically, risk nodes refer to device nodes with water hammer risks in the heating system extracted based on simulation results.

[0155] Specifically, according to the rectification cost of each first-level optimization plan, the revised heating system generates a corresponding optimization evaluation value based on the operation evaluation value generated by the simulation results. The larger the optimization evaluation value, the higher the benefit of the corresponding first-level optimization plan.

[0156] It can be understood that in the above embodiments, multiple optimization plans are formulated based on the simulation results of the heating system, and each optimization plan is simulated to evaluate the feasibility of different optimization plans (such as pipe diameter adjustment, valve type selection, pump performance optimization, etc.), and optimization parameters are formulated based on the evaluation results to reduce the risk of water hammer during the operation of the heating system, thereby improving the stability and safety of the operation of the heating system.

[0157] According to the first concept of the present application, an initial simulation model is constructed according to the equipment parameters of the heating system, and the initial simulation model is split according to the complexity of the heating system and the amount of simulation computing resources, thereby improving the accuracy and computational efficiency of the simulation results.

[0158] According to the second concept of the present application, a variety of optimization plans are formulated based on the simulation results of the heating system, and each optimization plan is simulated to evaluate the feasibility of different optimization plans (such as pipe diameter adjustment, valve type selection, pump performance optimization, etc.), and optimization parameters are formulated based on the evaluation results to reduce the risk of water hammer during the operation of the heating system, thereby improving the stability and safety of the operation of the heating system.

[0159] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. A method for simulating and analyzing water hammer risk in a heating system, characterized in that: include: Establish an initial simulation model of the heating system; Generate multiple simulation sub-models according to the initial simulation model and simulation operation resource parameters, and set simulation parameters of each simulation sub-model in turn; Generate the first-level simulation results of each simulation sub-model, and generate the optimization parameters of the heating system based on all the first-level simulation results.

2. The method for simulating and analyzing water hammer risk in a heating system according to claim 1, characterized in that: When building the initial simulation model, include: Obtain all equipment parameters of the heating system; Establish the initial geometric model; Obtain material properties of each device and generate material characteristic data packets; Set all boundary conditions and generate boundary condition data packages; Import material characteristics data package and boundary condition data package into the initial geometry model; Establish an initial simulation model based on the imported results.

3. The method for simulating and analyzing water hammer risk in a heating system according to claim 2, characterized in that: When generating multiple simulation sub-models, include: Generate a first reference value H1 according to a preset evaluation model; Among them, ai is the reference value of the i-th characteristic index in the initial simulation model; αi is the influencing factor of the i-th characteristic index; r is the number of characteristic indicators; Generate a second reference evaluation value H2 according to the simulation operation resource parameter; Generate a simulation evaluation value b according to the first reference evaluation value H1 and the second reference evaluation value H2; b=μ1*H1+μ2*H2; Wherein, μ1 is the preset first weight coefficient; μ2 is the preset second weight coefficient; The number n of simulation sub-models is set according to the simulation evaluation value b; Generate multiple simulation sub-models according to the number n of simulation sub-models; A simulation sub-model sequence P is established, where P = (p1, p2...pi...pn), wherein pi is the ith simulation sub-model.

4. The method for simulating and analyzing water hammer risk in a heating system according to claim 3, characterized in that: When setting the number of simulation sub-models n, it includes: Preset a first simulation evaluation value interval (B1, B2), preset a second simulation evaluation value interval (B2, B3) and a third simulation evaluation value interval (B3, B4); If the simulation evaluation value b is within the preset first simulation evaluation value interval, the number of simulation sub-models n is set to the preset first number value n1, that is, n=n1; If the simulation evaluation value b is within the preset second simulation evaluation value interval, the number of simulation sub-models n is set to the preset second number value n2, that is, n=n2; If the simulation evaluation value b is within the preset third simulation evaluation value interval, the number n of simulation sub-models is set to the preset third number value n3, that is, n=n3, and n1<n2<n3.

5. The method for simulating and analyzing water hammer risk in a heating system according to claim 3, characterized in that: When multiple simulation sub-models are generated according to the number of simulation sub-models n, it includes: The initial simulation model is segmented according to the number n of simulation sub-models, and a plurality of segmentation plans are generated; Establish a segmentation plan sequence C, C = (c1, c2...ci...cm), where ci is the i-th segmentation plan and m is the number of segmentation plans; Any single partitioning plan in the partitioning plan sequence C includes n simulation sub-models; Generate the operation evaluation value of each segmentation plan in turn; Establish an operation evaluation value sequence D, D = (d1, d2...di...dm), where di is the operation evaluation value of the i-th segmentation plan; Set the segmentation plan corresponding to the maximum value dmax in the running evaluation value sequence D as the first-level plan; A simulation sub-model sequence P is established according to the first-level plan.

6. The method for simulating and analyzing water hammer risk in a heating system according to claim 5, characterized in that: When generating the operation evaluation values ​​of each segmentation plan in sequence, it includes: Sequentially select the i-th segmentation plan as the target segmentation plan; Generate an operational evaluation value d of the target segmentation plan; Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second weight coefficient; Q3 is the preset third fixed coefficient; ki is the model evaluation value of the i-th simulation sub-model in the target segmentation plan; ji is the impact evaluation value between the i-th simulation sub-model and the i+1-th simulation sub-model; U is the reference value for the total simulation operation time and operation load parameter setting based on the target segmentation plan.

7. The method for simulating and analyzing water hammer risk in a heating system according to claim 3, characterized in that: When setting the simulation parameters of each simulation sub-model in turn, it includes: According to the simulation sub-model sequence P, pi is set as the target simulation sub-model in sequence; Generate a model evaluation value k of the target simulation sub-model; Among them, gi is the reference value of the i-th model evaluation index in the target simulation sub-model; βi is the influencing factor of the i-th model evaluation index; r1 is the number of model evaluation indicators; According to the model evaluation value, the simulation operation resource ratio and simulation operation time of the target simulation sub-model are set; Set the working condition simulation parameters of the target simulation sub-model; Generates the simulation operation sequence for each simulation sub-model.

8. The method for simulating and analyzing water hammer risk in a heating system according to claim 7, characterized in that: When generating the first-level simulation results for each simulation sub-model, it includes: Create a relationship table; Output the initial simulation results of each simulation sub-model in sequence according to the simulation operation order; Obtain the associated sub-model of the target simulation sub-model according to the association relationship table; Correcting the initial simulation result of the target simulation sub-model according to the initial simulation results of all associated sub-models, and generating the first-level simulation result of the target simulation sub-model; Generate the first-level simulation results of each target simulation sub-model in turn.

9. The method for simulating and analyzing water hammer risk in a heating system according to claim 8, characterized in that: When generating the optimal parameters of the heating system based on all the first-level simulation results, it includes: Generate multiple risk nodes based on all first-level simulation results; Establish multiple first-level optimization plans based on all risk nodes; Simulate each first-level optimization plan in turn, and generate an optimization evaluation value of each first-level optimization plan according to the simulation results; Establish the optimized evaluation value sequence F, F = (f1, f2…f i …f m1 ), where f i is the optimization evaluation value of the i-th first-level optimization plan; m1 is the number of first-level optimization plans; Set the first-level optimization plan corresponding to the maximum value fi in the optimization evaluation value sequence F as the second-level optimization plan; Generate the optimization parameters of the heating system according to the secondary optimization plan.

Images