Water hammer risk simulation analysis method for heat supply system

Abstract

The present application relates to the technical field of heat supply systems, and in particular to a water hammer risk simulation analysis method for a heat supply system. The method comprises: establishing an initial simulation model of a heat supply system; generating a plurality of simulation sub-models on the basis of the initial simulation model and simulation operating resource parameters, and successively setting simulation parameters of the simulation sub-models; and generating primary simulation results of the simulation sub-models and, on the basis of all of the primary simulation results, generating optimization parameters of the heat supply system. The initial simulation model is constructed on the basis of apparatus parameters of the heat supply system and, on the basis of the degree of complexity of the heat supply system and the quantity of simulation operating resources, the initial simulation model is split, so as to improve the accuracy and computing efficiency of the simulation results, and reduce water hammer risks during the operation of the heat supply system, thus improving the operating stability and safety of the heat supply system.

Description

A simulation analysis method for water hammer risk of a heating system TECHNICAL FIELD

[0001] The present application relates to the technical field of heating systems, in particular to a simulation analysis method for water hammer risk of a heating system. BACKGROUND

[0002] In a heating system, water hammer phenomenon will affect the safe operation of the system. The large amplitude pressure fluctuation caused by water hammer phenomenon will cause the system to have pressure transient rise and fall phenomenon. When the pressure in the pipe network instantaneously rises, the equipment, pipes and pipe accessories may be damaged due to insufficient pressure bearing capacity. In addition, when the pressure in the pipe network instantaneously drops, the high-temperature hot water transported by the heating network may vaporize to form cavities, which will destroy the continuity of the water flow. When the cavities in the pipe network meet the rising pressure wave, the cavities collapse to form a new pressure transient process, which will affect the safe operation of the heating system.

[0003] Water hammer phenomenon is most likely to occur during pump stopping, restarting, and improper valve opening and closing during power failure. In particular, for pipe networks with high medium temperature, long transportation distance, and large drop, measures should be taken to prevent water hammer phenomenon and minimize water hammer damage. SUMMARY

[0004] The purpose of the present application is to solve the above technical problems. The present application provides a simulation analysis method for water hammer risk of a heating system, which aims 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 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 operation resources, thereby improving the accuracy and computational efficiency of the simulation results.

[0006] In some embodiments of the present application, according to the simulation results of the heating system, a plurality 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.), and optimization parameters are formulated according to the evaluation results to reduce the water hammer risk 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 simulation analysis method for water hammer risk of a heating system is provided, which comprises:

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

[0009] generating a plurality of simulation sub-models according to the initial simulation model and simulation operation resource parameters, and setting simulation parameters of each simulation sub-model in sequence;

[0010] generating a first simulation result of each simulation sub-model, and generating an optimization parameter of the heating system according to all the first simulation results.

[0011] In some embodiments of the present application, when the initial simulation model is established, the following steps are included:

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

[0013] establishing an initial geometric model;

[0014] obtaining material properties of each equipment and generating a material characteristic data package;

[0015] setting all the boundary conditions and generating a boundary condition data package;

[0016] importing the material characteristic data package and the boundary condition data package into the initial geometric model;

[0017] establishing the initial simulation model according to the importing result.

[0018] In some embodiments of the present application, when the plurality of simulation sub-models are generated, the following steps are included:

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

[0020] ;

[0021] wherein ai is a reference value of the i-th characteristic index in the initial simulation model; ai is an influence factor of the i-th characteristic index; r is the number of characteristic indexes;

[0022] generating a second reference evaluation value H2 according to the simulation running resource parameters;

[0023] generating 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 a preset first weight coefficient; µ2 is a preset second weight coefficient;

[0026] setting a number n of simulation sub-models according to the simulation evaluation value b;

[0027] generating a plurality of simulation sub-models according to the number n of simulation sub-models;

[0028] establishing a simulation sub-model sequence P, P = (p1, p2…pi…pn), wherein pi is the i-th simulation sub-model.

[0029] In some embodiments of the present application, when the number n of simulation sub-models is set, the following steps are included:

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

[0031] If the simulation evaluation value b is in the preset first simulation evaluation value interval, the simulation sub-model number n is set as a preset first number value n1, that is, n = n1;

[0032] If the simulation evaluation value b is in the preset second simulation evaluation value interval, the simulation sub-model number n is set as a preset second number value n2, that is, n = n2;

[0033] If the simulation evaluation value b is in the preset third simulation evaluation value interval, the simulation sub-model number n is set as a preset third number value n3, that is, n = n3, and n1 < n2 < n3.

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

[0035] segmenting the initial simulation model according to the simulation sub-model number n, and generating a plurality of segmentation plans;

[0036] establishing a segmentation plan sequence C, C = (c1, c2…ci…cm), wherein ci is the i th segmentation plan, and m is the number of segmentation plans;

[0037] any single segmentation plan in the segmentation plan sequence C comprises n simulation sub-models;

[0038] generating running evaluation values of each segmentation plan in sequence;

[0039] establishing a running evaluation value sequence D, D = (d1, d2…di…dm), wherein di is the running evaluation value of the i th segmentation plan;

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

[0041] establishing a simulation sub-model sequence P according to the first-level plan.

[0042] In some embodiments of the present application, when the running evaluation values of each segmentation plan are generated in sequence, the method comprises:

[0043] selecting the i th segmentation plan as a target segmentation plan in sequence;

[0044] generating a running evaluation value d of the target segmentation plan;

[0045] ;

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

[0047] In some embodiments of the present application, when the simulation parameters of each simulation sub-model are sequentially set, the following steps are included:

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

[0049] A model evaluation value k of the target simulation sub-model is generated;

[0050] ;

[0051] Wherein, gi is a reference value of an i-th model evaluation index in the target simulation sub-model; βi is an influence factor of the i-th model evaluation index; and r1 is a number of model evaluation indexes.

[0052] According to the model evaluation value, a simulation running resource proportion and a simulation running time length of the target simulation sub-model are set;

[0053] A working condition simulation parameter of the target simulation sub-model is set;

[0054] A simulation operation sequence of each simulation sub-model is generated.

[0055] In some embodiments of the present application, when the first-level simulation result of each simulation sub-model is generated, the following steps are included:

[0056] An association relationship table is established;

[0057] According to the simulation operation sequence, an initial simulation result of each simulation sub-model is sequentially outputted;

[0058] According to the association relationship table, an associated sub-model of the target simulation sub-model is obtained;

[0059] According to the initial simulation results of all associated sub-models, the initial simulation result of the target simulation sub-model is corrected, and a first-level simulation result of the target simulation sub-model is generated;

[0060] The first-level simulation result of each target simulation sub-model is sequentially generated.

[0061] In some embodiments of the present application, when the optimization parameters of the heating system are generated according to all first-level simulation results, the following steps are included:

[0062] generating a plurality of risk nodes according to all the first simulation results;

[0063] establishing a plurality of first optimization plans according to all the risk nodes;

[0064] sequentially simulating each first optimization plan, and generating an optimization evaluation value of each first optimization plan according to the simulation result;

[0065] establishing an optimization evaluation value sequence F, F=(f1, f2…f i …f m1 ), wherein f i is the optimization evaluation value of the i-th first optimization plan; and m1 is the number of the first optimization plans;

[0066] setting the first optimization plan corresponding to the maximum value fi in the optimization evaluation value sequence F as a second optimization plan;

[0067] generating an optimization parameter of the heating system according to the second optimization plan.

[0068] Compared with the prior art, the heating system water hammer risk simulation analysis method provided in the embodiments of the present application has the following beneficial effects:

[0069] According to the equipment parameters of the heating system, an initial simulation model is constructed, and the initial simulation model is split according to the complexity of the heating system and the amount of simulation operation resources, so as to improve the accuracy and calculation efficiency of the simulation result.

[0070] According to the simulation result of the heating system, a plurality 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.), and an optimization parameter is formulated according to the evaluation result to reduce the water hammer risk in the operation process of the heating system, so as to improve the stability and safety of the operation of the heating system. BRIEF DESCRIPTION OF DRAWINGS

[0071] FIG. 1 is a flowchart of a heating system water hammer risk simulation analysis method according to an embodiment of the present application. DETAILED DESCRIPTION

[0072] The specific embodiments of the present application will be further described in detail below with reference to the accompanying drawings and embodiments. The following embodiments are used to illustrate the present application, but not to limit the scope of the present application.

[0073] In the description of the present application, it needs to be understood that the terms "center", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0074] The terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0075] In the description of the present application, it needs to be explained that, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through intermediate medium, or the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0076] As shown in FIG. 1, a water hammer risk simulation analysis method of a heat supply system according to an embodiment of the present application preferably comprises:

[0077] S101: establishing an initial simulation model of the heat supply system;

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

[0079] S103: generating a first simulation result of each simulation sub-model, and generating an optimization parameter of the heat supply system according to all first simulation results.

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

[0081] Obtaining all device parameters of the heat supply system;

[0082] Establishing an initial geometric model;

[0083] Obtaining material properties of each device and generating a material characteristic data package;

[0084] Setting all boundary conditions and generating a boundary condition data package;

[0085] importing the material characteristic data package and the boundary condition data package into the initial geometric model;

[0086] establishing the initial simulation model according to the importing result.

[0087] Specifically, the initial three-dimensional assembly model is established according to the equipment parameters of the heating system, and the equipment includes pipes, valves, pumps, heat exchangers and the like.

[0088] Specifically, the physical properties of the equipment such as pipes, valves, pumps and the like are input, such as the elastic modulus, density, thermal conductivity and the like of the material. The boundary conditions of the system are defined, such as the inlet pressure, temperature, flow and the like, and the outlet conditions. The initial state of the simulation is set, such as the state when the system starts or when the valve is closed. Thus, the initial simulation model is constructed.

[0089] Specifically, the initial simulation model includes all the areas of the heating system.

[0090] In the preferred embodiment of the present application, when generating a plurality of simulation sub-models, the following steps are included:

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

[0092] ;

[0093] wherein ai is the reference value of the i-th characteristic index in the initial simulation model; ai is the influence factor of the i-th characteristic index; r is the number of characteristic indexes;

[0094] generating a second reference evaluation value H2 according to the simulation running resource parameters;

[0095] generating 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 a preset first weight coefficient; µ2 is a preset second weight coefficient;

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

[0099] generating a plurality of simulation sub-models according to the number n of simulation sub-models;

[0100] establishing a simulation sub-model sequence P, P = (p1, p2…pi…pn), wherein pi is the i-th 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 heat supply system, the greater the corresponding first reference evaluation value, the fewer the simulation running resources, and the greater the corresponding second reference evaluation value.

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

[0103] Specifically, the greater the simulation evaluation value, the lower the efficiency of the current heat supply system simulation operation.

[0104] Specifically, a single simulation sub-model includes a partial area of the heat supply system, which can perform local simulation analysis on the heat supply system. All simulation sub-models in the simulation sub-model sequence P can complete the complete analysis of the heat supply system.

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

[0106] The first simulation evaluation value interval (B1, B2) is preset, the second simulation evaluation value interval (B2, B3) and the third simulation evaluation value interval (B3, B4) are preset;

[0107] If the simulation evaluation value b is in 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 in 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 in the preset third simulation evaluation value interval, the number of simulation sub-models n 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, thereby segmenting the constructed initial simulation model to generate multiple simulation sub-models, and each simulation sub-model is simulated and analyzed, thereby improving the accuracy and efficiency of the simulation analysis of the heat supply system.

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

[0112] Segmenting the initial simulation model according to the number of simulation sub-models n and generating multiple segmentation plans;

[0113] A segmentation plan sequence C is established, C=(c1, c2…ci…cm), wherein ci is the i-th segmentation plan, and m is the number of segmentation plans;

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

[0115] The running evaluation value of each segmentation plan is generated in sequence;

[0116] A running evaluation value sequence D is established, D=(d1, d2…di…dm), wherein di is the running evaluation value of the i-th segmentation plan;

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

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

[0119] Specifically, according to the equipment nodes in the heating system, a plurality of segmentation plans are generated, and all simulation sub-models in a single segmentation plan can comprehensively simulate and analyze the heating system.

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

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

[0122] Specifically, when the running evaluation value of each segmentation plan is generated in sequence, it includes:

[0123] The i-th segmentation plan is selected as the target segmentation plan in sequence;

[0124] The running evaluation value d of the target segmentation plan is generated;

[0125] ;

[0126] Wherein e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; e3 is a preset third weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second weight coefficient; Q3 is a 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 influence evaluation value between the i-th simulation sub-model and the i+1-th simulation sub-model; U is a reference value based on the simulation operation total time and operation load parameter settings of the target segmentation plan.

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

[0128] Specifically, the total simulation time is the time length for all simulation sub-models in a single partition task to complete simulation simulation. The smaller the total simulation operation time, the larger the corresponding reference value U. The operation load parameter refers to the average occupation and peak occupation of resources 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 operation resources, thereby improving the accuracy and calculation efficiency of the simulation result.

[0130] In the preferred embodiment of the present application, when the simulation parameters of each simulation sub-model are set in turn, it includes:

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

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

[0133] ;

[0134] Wherein, gi is the reference value of the i-th model evaluation index in the target simulation sub-model; βi is the influence factor of the i-th model evaluation index; r1 is the number of model evaluation indexes;

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

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

[0137] Generate the simulation operation sequence of each simulation sub-model.

[0138] Specifically, the model evaluation index includes but is not limited to the number of devices in the corresponding area of the simulation sub-model, the water flow intensity, the number of adjacent simulation sub-models and other parameters.

[0139] Specifically, when setting the working condition simulation parameters, the main target of simulation needs to be clear, such as predicting the peak pressure of water flow, evaluating the damage risk of pipeline, etc.

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

[0141] Establish an association table;

[0142] The initial simulation results of each simulation sub-model are output sequentially according to the simulation operation order;

[0143] Obtain the associated sub-models of the target simulation sub-model based on the association table;

[0144] The initial simulation results of the target simulation sub-model are corrected based on the initial simulation results of all associated sub-models, and the first-level simulation results of the target simulation sub-model are generated.

[0145] The first-level simulation results of each target simulation sub-model are generated sequentially.

[0146] Specifically, depending on whether the corresponding regions of each simulation sub-model are adjacent, if they are adjacent, the two corresponding simulation sub-models are related sub-models, and an association table is generated based on all the association relationships.

[0147] In a preferred embodiment of this application, when generating optimized parameters for the heating system based on all first-level simulation results, the following steps are included:

[0148] Multiple risk nodes are generated based on all first-level simulation results;

[0149] Establish multiple primary optimization plans based on all risk nodes;

[0150] Each first-level optimization plan is simulated sequentially, and the optimization evaluation value of each first-level optimization plan is generated based on the simulation results;

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

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

[0153] Optimization parameters for the heating system are generated based on the secondary optimization plan.

[0154] Specifically, risk nodes refer to equipment nodes in the heating system that pose a risk of water hammer, as extracted based on simulation results.

[0155] Specifically, based on the rectification costs of each primary optimization plan, the corrected heating system generates a corresponding optimization evaluation value based on the operational evaluation value generated from the simulation results. The higher the optimization evaluation value, the higher the benefit of the corresponding primary optimization plan.

[0156] It can be understood that, in the above embodiments, according to the simulation result of the heating system, a plurality of optimization plans are formulated, and each optimization plan is simulated and evaluated for the feasibility of different optimization plans (such as adjustment of pipe diameter, selection of valve type, performance optimization of pump, etc.), and optimization parameters are formulated according to the evaluation result to reduce the water hammer risk in 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 operation resources, thereby improving the accuracy and calculation efficiency of the simulation result.

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

[0159] The above is only the preferred embodiment of the present application, and it should be pointed out that, for ordinary skilled in the art, without departing from the technical principles of the present application, a number of improvements and substitutions can be made, and these improvements and substitutions should be considered as the protection scope of the present application.

Claims

1. A method for simulating and analyzing water hammer risk of a heating system, characterized by, The application relates to a method for optimizing a heating system, and belongs to the field of simulation technology. The method comprises the following steps: establishing an initial simulation model of the heating system; generating a plurality of simulation sub-models according to the initial simulation model and simulation running resource parameters, and setting simulation parameters of the simulation sub-models in sequence; 2. The heat supply system water hammer risk simulation analysis method according to claim 1, characterized in that, generating first simulation results of the simulation sub-models, and generating optimization parameters of the heating system according to all the first simulation results. When the initial simulation model is established, the following steps are included: obtaining all equipment parameters of the heating system; establishing an initial geometric model; obtaining material attributes of each equipment, and generating a material characteristic data package; setting all boundary conditions, and generating a boundary condition data package; importing the material characteristic data package and the boundary condition data package into the initial geometric model; 3. The heat supply system water hammer risk simulation analysis method according to claim 2, characterized in that, establishing the initial simulation model according to the importing result. When the plurality of simulation sub-models are generated, the following steps are included: ； generating a first reference value H1 according to a preset evaluation model; wherein ai is a reference value of an i-th characteristic index in the initial simulation model; ai is an influence factor of the i-th characteristic index; and r is a characteristic index quantity; generating a second reference value H2 according to the simulation running resource parameters; generating a simulation evaluation value b according to the first reference value H1 and the second reference value H2; b = µ1*H1 + µ2*H2; wherein µ1 is a preset first weight coefficient; and µ2 is a preset second weight coefficient; setting a simulation sub-model quantity n according to the simulation evaluation value b; generating a plurality of simulation sub-models according to the simulation sub-model quantity n; 4. The heat supply system water hammer risk simulation analysis method according to claim 3, characterized in that, establishing a simulation sub-model sequence P, P = (p1, p2…pi…pn), wherein pi is an i-th simulation sub-model. When the simulation sub-model quantity n is set, the following steps are included: presetting a first simulation evaluation value interval (B1, B2), a second simulation evaluation value interval (B2, B3) and a third simulation evaluation value interval (B3, B4); if the simulation evaluation value b is in the preset first simulation evaluation value interval, setting the simulation sub-model quantity n as a preset first quantity value n1, that is, n = n1; if the simulation evaluation value b is in the preset second simulation evaluation value interval, setting the simulation sub-model quantity n as a preset second quantity value n2, that is, n = n2; 5. The heat supply system water hammer risk simulation analysis method according to claim 3, characterized in that, if the simulation evaluation value b is in the preset third simulation evaluation value interval, setting the simulation sub-model quantity n as a preset third quantity value n3, that is, n = n3, and n1 < n2 < n3. When the plurality of simulation sub-models are generated according to the simulation sub-model quantity n, the following steps are included: segmenting the initial simulation model according to the simulation sub-model quantity n, and generating a plurality of segmentation plans; establishing a segmentation plan sequence C, C = (c1, c2…ci…cm), wherein ci is an i-th segmentation plan, and m is a segmentation plan quantity; any single segmentation plan in the segmentation plan sequence C comprises n simulation sub-models; generating running evaluation values of the segmentation plans in sequence; establishing a running evaluation value sequence D, D = (d1, d2…di…dm), wherein di is a running evaluation value of an i-th segmentation plan; setting a segmentation plan corresponding to a maximum value dmax in the running evaluation value sequence D as a first-level plan; 6. The heat supply system water hammer risk simulation analysis method according to claim 5, characterized in that, establishing the simulation sub-model sequence P according to the first-level plan. When the running evaluation values of the segmentation plans are generated in sequence, the following steps are included: selecting an i-th segmentation plan as a target segmentation plan in sequence; Generate the running evaluation value d of the target segmentation plan; ；； Wherein, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; e3 is a preset third weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second weight coefficient; Q3 is a 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 influence evaluation value between the i th simulation sub model and the i+1 th simulation sub model; U is a reference value set based on the total simulation operation time and operation load parameters of the target segmentation plan.

7. The heat supply system water hammer risk simulation analysis method according to claim 3, characterized in that, When setting the simulation parameters of each simulation sub model in turn, it includes: Set pi as the target simulation sub model according to the simulation sub model sequence P; Generate the model evaluation value k of the target simulation sub model; ； Wherein, gi is the reference value of the i th model evaluation index in the target simulation sub model; βi is the influence factor of the i th model evaluation index; r1 is the number of model evaluation indexes; Set the simulation running resource proportion and simulation running time of the target simulation sub model according to the model evaluation value; Set the working condition simulation parameters of the target simulation sub model; Generate the simulation operation sequence of each simulation sub model.

8. The heat supply system water hammer risk simulation analysis method according to claim 7, characterized in that, When generating the first level simulation result of each simulation sub model, it includes: Establish the association relationship table; Output the initial simulation result of each simulation sub model in turn according to the simulation operation sequence; Get the associated sub model of the target simulation sub model according to the association relationship table; Correct the initial simulation result of the target simulation sub model according to the initial simulation result of all associated sub models, and generate the first level simulation result of the target simulation sub model; Generate the first level simulation result of each target simulation sub model in turn.

9. The heat supply system water hammer risk simulation analysis method according to claim 8, characterized in that, When generating the optimization parameters of the heating system according to all first level simulation results, it includes: Generate multiple risk nodes according to all first level simulation results; Establish multiple first level optimization plans according to all risk nodes; Simulate each first level optimization plan in turn, and generate the optimization evaluation value of each first level optimization plan according to the simulation result; An optimized evaluation value sequence F, F=(f1, f2…f i …f m1 ), is established, wherein f i is an optimized evaluation value of an i-th primary optimization plan; m1 is a primary optimization plan quantity; 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 second level optimization plan.

Images