Method and device for automatically generating heat balance diagram of energy system

Abstract

The invention discloses a method and a device for automatically generating a heat balance diagram of an energy system, and belongs to the technical field of energy systems. The method comprises the steps of constructing a basic database based on historical project data of a target energy system, and obtaining a plurality of training samples; the training sample comprises data of boundary parameters and characteristic parameters as input and a heat balance graph as output; utilizing the plurality of training samples to train a heat balance model, so that the heat balance model determines a graph body based on the data of the boundary parameters in the training samples, and predicts thermal parameters of each graph node in the graph body based on the data of the boundary parameters and the data of the characteristic parameters in the training samples, adjusting model parameters based on the prediction result and the loss between the thermal parameters of the graph nodes on the thermal balance graph in the training sample to obtain a trained thermal balance model; and outputting the heat balance map for the input data of the to-be-generated heat balance map by using the heat balance model. According to the method, the required heat balance diagram can be automatically obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of energy systems, and particularly to a method and device for automatically generating a heat balance diagram of an energy system. Background Art

[0002] The calculation of a heat balance diagram is a core link in thermodynamic analysis and energy system design, and is widely used in process calculations of complex energy systems such as thermal power generation, gas-steam combined cycle, nuclear power generation, compressed air energy storage systems, etc. By describing the flow and transformation relationships of energy and matter in the system, the heat balance diagram provides an important theoretical basis for system design, optimization, and operation. Its calculation results can provide the thermodynamic parameters of each node in the process, including key data such as pressure, temperature, enthalpy value, and flow rate. These data are the "command diagrams" for subsequent detailed system design and directly determine the performance, efficiency, and reliability of the system. Therefore, the accuracy and speed of heat balance diagram calculation have a crucial impact on the implementation of engineering projects.

[0003] The calculation of a heat balance diagram in related technologies highly depends on the empirical knowledge of designers, which not only has low efficiency but also easily introduces human errors. Summary of the Invention

[0004] The present invention provides a method and device for automatically generating a heat balance diagram of an energy system. The technical solutions are as follows:

[0005] On the one hand, a method for automatically generating a heat balance diagram of an energy system is provided. The method includes:

[0006] Based on the historical project data of the target energy system, a basic database is constructed; the basic database includes multiple types of heat balance diagrams corresponding to the target energy system; the heat balance diagram includes a graphic ontology and the thermodynamic parameters marked on the graphic nodes, and the graphic ontology is formed by multiple graphic nodes and their connection relationships;

[0007] Based on the basic database, a plurality of training samples are obtained; the training samples include data of boundary parameters and characteristic parameters as inputs, and also include heat balance diagrams as outputs;

[0008] Using the plurality of training samples to train a heat balance model, so that the heat balance model determines the graphic ontology of the heat balance diagram to be output based on the data of the boundary parameters in the training samples, and predicts the thermodynamic parameters of each graphic node in the graphic ontology based on the data of the boundary parameters and the data of the characteristic parameters in the training samples, and adjusts the model parameters based on the loss between the prediction result and the thermodynamic parameters of the graphic nodes on the heat balance diagram in the training samples to obtain a trained heat balance model;

[0009] Output a heat balance diagram for the input data for which a heat balance diagram is to be generated using the heat balance model; the input data is input according to boundary parameters and characteristic parameters.

[0010] On the other hand, an automatic generation device for a heat balance diagram of an energy system is provided. The device includes:

[0011] A construction unit for constructing a basic database based on the historical project data of the target energy system; the basic database includes multiple types of heat balance diagrams corresponding to the target energy system; the heat balance diagram includes a graphic ontology and thermodynamic parameters marked on the graphic nodes, and the graphic ontology is formed by multiple graphic nodes and their connection relationships;

[0012] An acquisition unit for acquiring a plurality of training samples based on the basic database; the training samples include data of boundary parameters and characteristic parameters as inputs, and also include heat balance diagrams as outputs;

[0013] A training unit for training a heat balance model using a plurality of the training samples, so that the heat balance model determines the graphic ontology of the heat balance diagram to be output based on the data of the boundary parameters in the training samples, and predicts the thermodynamic parameters of each graphic node in the graphic ontology based on the data of the boundary parameters and the data of the characteristic parameters in the training samples, and adjusts the model parameters based on the loss between the prediction result and the thermodynamic parameters of the graphic nodes on the heat balance diagram in the training samples to obtain a trained heat balance model;

[0014] A heat balance calculation unit for outputting a heat balance diagram for the input data for which a heat balance diagram is to be generated using the heat balance model; the input data is input according to boundary parameters and characteristic parameters.

[0015] On the other hand, a computer device is provided. The computer device includes a memory and a processor. The memory is used to store a computer program, and the processor is used to execute the computer program stored on the memory to implement the steps of the above-mentioned automatic generation method of the heat balance diagram of the energy system.

[0016] On the other hand, a computer-readable storage medium is provided. The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned automatic generation method of the heat balance diagram of the energy system are implemented.

[0017] On the other hand, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned automatic generation method of the heat balance diagram of the energy system are implemented.

[0018] The technical solution provided by the present invention can at least bring the following beneficial effects:

[0019] By constructing a basic database by using the historical project information of the target energy system, and then obtaining training samples from the basic database to train the heat balance model. After the training is completed, only by inputting the data of the boundary parameters and the data of the characteristic parameters into the trained heat balance model, the heat balance diagram output by the heat balance model can be obtained. In this solution, the operator can adjust and input the data of the input boundary parameters and characteristic parameters, and then automatically and quickly obtain the required heat balance diagram without the operator having design experience, which can ensure accuracy and improve the generation efficiency. Brief Description of the Drawings

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 It is a flowchart of an automatic generation method for a heat balance diagram of an energy system provided by an embodiment of the present invention;

[0022] Figure 2 It is a structural diagram of a heat balance model provided by an embodiment of the present invention;

[0023] Figure 3 It is a structural diagram of an automatic generation device for a heat balance diagram of an energy system provided by an embodiment of the present invention;

[0024] Figure 4 It is a hardware architecture diagram of a computer device provided by an embodiment of the present invention. Detailed Embodiments

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0026] Please refer to Figure 1 , an automatic generation method for a heat balance diagram of an energy system provided by an embodiment of the present invention, the method includes:

[0027] Step 100: Construct a basic database based on the historical project data of the target energy system; the basic database includes thermal balance diagrams of multiple types corresponding to the target energy system; the thermal balance diagram includes a graphic ontology and thermal parameters marked on the graphic nodes, and the graphic ontology is formed by multiple graphic nodes and their connection relationships;

[0028] Step 102: Obtain multiple training samples based on the basic database; the training samples include data of boundary parameters and characteristic parameters as inputs, and also include thermal balance diagrams as outputs;

[0029] Step 104: Use multiple training samples to train the thermal balance model, so that the thermal balance model determines the graphic ontology of the to-be-output thermal balance diagram based on the data of the boundary parameters in the training samples, predicts the thermal parameters of each graphic node in the graphic ontology based on the data of the boundary parameters and the data of the characteristic parameters in the training samples, and adjusts the model parameters based on the loss between the prediction result and the thermal parameters of the graphic nodes on the thermal balance diagram in the training samples to obtain a trained thermal balance model;

[0030] Step 106: Use the thermal balance model to output a thermal balance diagram for the input data to be used to generate a thermal balance diagram; the input data is input according to boundary parameters and characteristic parameters.

[0031] In the embodiments of the present invention, by using the historical project information of the target energy system to construct a basic database, and then obtaining training samples from the basic database to train the thermal balance model. After training, only by inputting the data of the boundary parameters and the data of the characteristic parameters into the trained thermal balance model, the thermal balance diagram output by the thermal balance model can be obtained. In this solution, the operator can adjust and input the data of the boundary parameters and the characteristic parameters, and then quickly obtain the required thermal balance diagram without the operator having design experience, which can ensure accuracy and improve the generation efficiency.

[0032] The following describes Figure 1 the execution manners of the following steps.

[0033] First, steps 100 "Construct a basic database based on the historical project data of the target energy system" and 102 "Obtain multiple training samples based on the basic database" are described simultaneously.

[0034] The calculation of the thermal balance diagram not only requires historical project data, but also depends on the experience of designers and specific software. Moreover, not all designers who need the thermal balance diagram of the energy system can obtain historical project data. Therefore, a method for automatically generating a thermal balance diagram needs to be provided.

[0035] Considering that the mechanism model of the energy system can be used to calculate thermodynamic parameters, however, the thermodynamic parameters calculated directly using the mechanism model are inconsistent with the actual project data. It can be seen that in the actual project application, there are other complex associations among the components in the energy system. Therefore, it is possible to consider using an AI model to learn the characteristics of this complex association to improve the accuracy of automatically generating a heat balance diagram.

[0036] In order to be able to automatically generate a heat balance diagram, it is necessary for professionals to construct a basic database based on thermodynamic expertise using the historical project data of the target energy system, which is used to provide training samples for training the heat balance model, so as to automatically generate a heat balance diagram using the heat balance model.

[0037] In the embodiments of the present invention, the target energy system can at least be a thermal power generation system, a compressed air energy storage system, a gas-steam combined cycle system or a nuclear power generation system.

[0038] Specifically, the target energy system can be classified based on the historical project data of the target energy system. For example, the above-mentioned target energy system can be divided into the following types:

[0039] The thermal power generation system can be divided into types such as subcritical 300MW units, supercritical 300MW units, ultra-supercritical 300MW units, subcritical 600MW units, supercritical 600MW units, ultra-supercritical 600MW units, subcritical 1000MW units, supercritical 1000MW units, ultra-supercritical 1000MW units, etc.;

[0040] The compressed air energy storage system can be divided into types such as medium-temperature 100MW grade, high-temperature 100MW grade, medium-temperature 200MW grade, high-temperature 200MW grade, medium-temperature 300MW grade, high-temperature 300MW grade, etc.;

[0041] The gas-steam combined cycle system can be divided into different types composed of different capacities and configurations;

[0042] The nuclear power generation system can be divided into different types composed of reactor and steam cycle systems with different capacities of units.

[0043] For each type of target energy system, boundary parameters and characteristic parameters can be determined based on the historical project information of the corresponding type, and a corresponding heat balance diagram can be constructed using the data of the boundary parameters and the data of the characteristic parameters, thereby obtaining a basic database. The basic database includes heat balance diagrams corresponding to multiple types of the target energy system; the heat balance diagram includes a graphic ontology and thermodynamic parameters marked on the graphic nodes, and the graphic ontology is formed by multiple graphic nodes and their connection relationships; the graphic nodes are used to represent components in the target energy system, and the connection relationships are used to represent parameter transfer relationships; the parameter transfer relationships include energy transfer relationships, mass transfer relationships, and kinetic energy transfer relationships.

[0044] Boundary parameters are external constraint conditions defined during the modeling or analysis of an energy system, used to describe the input / output limitations of the interaction between the energy system and the outside world. For example, for a control valve, its boundary parameters may include inlet pressure, inlet specific enthalpy, inlet flow rate, etc.; for a compressor, its boundary parameters may include inlet temperature, inlet dryness, inlet specific enthalpy, specific entropy, volume flow rate, pressure ratio, etc.; for a turbine pressure unit, its boundary parameters may include steam inlet pressure, steam inlet specific enthalpy, unit flow rate, back pressure, etc.

[0045] Characteristic parameters are key variables that describe the inherent properties or dynamic behaviors within an energy system, used to reflect the physical, chemical, or functional characteristics of the energy system itself. For example, for a control valve, its characteristic parameters may include relative pressure loss, low-pressure leakage area, low-pressure leakage flow coefficient, high-pressure leakage area, high-pressure leakage flow coefficient; for a compressor, its characteristic parameters may include isentropic efficiency, etc., and for a turbine pressure unit, its characteristic parameters may include isentropic efficiency, flow area, flow capacity correction coefficient, etc.

[0046] In the embodiment of the present invention, when the boundary parameters are determined, the components included in the energy system and the connection relationships between the components can be determined. This connection relationship is the parameter transfer relationship between the components in the energy system, and each component is used to represent a graphic node. Therefore, the boundary parameters determine the graphic ontology in the heat balance diagram. In addition, the graphic ontologies of the heat balance diagrams of the same type of target energy system are the same.

[0047] After constructing the above basic database, training samples can be directly extracted. Among them, the training samples include the data of the boundary parameters and the data of the characteristic parameters as inputs, and also include the heat balance diagram as an output.

[0048] Then, steps 104 and 106 will be described simultaneously.

[0049] Since the boundary parameters determine the graphical body of the heat balance diagram, in the embodiments of the present invention, when training the heat balance model using multiple training samples, the heat balance model can determine the graphical body of the to-be-output heat balance diagram based on the data of the boundary parameters in the training samples.

[0050] In one implementation, a mapping relationship between the data range of the boundary parameters and the graphical body can be established in advance according to different types of heat balance diagrams in the basic database, and the heat balance model determines the graphical body of the to-be-output heat balance diagram based on this mapping relationship and the input data of the boundary parameters.

[0051] In another implementation, considering that the historical project data is limited and it is impossible to cover all types of heat balance diagrams for the target energy system, during the training process of the heat balance model, the heat balance model needs to learn the mapping relationship between the data of the boundary parameters and the graphical body.

[0052] In addition, the thermal parameters of each graphical node in the graphical body are jointly obtained based on the data of the boundary parameters and the data of the characteristic parameters. Based on this, in the embodiments of the present invention, please refer to Figure 2 , the heat balance model at least includes: a first prediction sub-model and a second prediction sub-model; the first prediction sub-model is used to predict the graphical body of the to-be-output heat balance diagram; the second prediction sub-model is used to predict the corresponding thermal parameters for the graphical nodes in the predicted graphical body output by the first prediction sub-model.

[0053] To ensure the training accuracy of the heat balance, in the embodiments of the present invention, the first prediction sub-model and the second prediction sub-model are trained by a joint training method;

[0054] The joint training method includes:

[0055] For each training sample, perform: input the data of the boundary parameters in the training sample into the first prediction sub-model, and obtain the predicted graphical body output by the first prediction sub-model; input the predicted graphical body, the data of the boundary parameters, and the data of the characteristic parameters in the training sample into the second prediction sub-model, so that the second prediction sub-model outputs the predicted thermal parameters corresponding to each graphical node in the predicted graphical body; jointly adjust the model parameters of the first prediction sub-model and the second prediction sub-model using the first contrast loss and the second contrast loss; the first contrast loss is the contrast loss between the predicted graphical body and the graphical body of the heat balance diagram in the training sample, and the second contrast loss is the contrast loss between the predicted thermal parameters and the thermal parameters of each graphical node on the heat balance diagram in the training sample.

[0056] In the embodiments of the present invention, since the first prediction sub-model and the second prediction sub-model are trained in a joint training manner, and the prediction accuracy of the second prediction sub-model has a strong correlation with the accuracy of the predicted graph ontology output by the first prediction sub-model, when adjusting the model parameters, the first prediction sub-model and the second prediction sub-model are regarded as a whole, and then the first contrast loss and the second contrast loss are jointly used to adjust the model parameters of the overall heat balance model, so that the trained heat balance model can take into account the prediction of the graph ontology and the prediction of the thermal parameters, making the prediction results more accurate.

[0057] Further, considering the relationship between the graph nodes in the heat balance diagram corresponding to the energy system, the second contrast calculation can be obtained in the following manner:

[0058] Determine the parameter transfer relationship based on the connection relationship of the graph nodes in the predicted graph ontology; the parameter transfer relationship includes energy transfer relationship, mass transfer relationship and kinetic energy transfer relationship;

[0059] Based on the parameter transfer relationship, predict the thermal parameters for each graph node in turn, and calculate the third contrast loss between the predicted thermal parameters of each graph node and the thermal parameters in the training sample. Determine the sum of the third contrast losses corresponding to each of the multiple graph nodes as the second contrast loss.

[0060] In the embodiments of the present invention, according to the parameter transfer relationship, when predicting the thermal parameters of the graph nodes, the thermal parameters are related to the input energy, input mass, input kinetic energy, output energy, output mass and output kinetic energy. Therefore, according to the parameter transfer relationship, the thermal parameters of each graph node are predicted in turn. Since the thermal parameters of each graph node on the graph ontology need to be predicted, the sum of the third contrast losses corresponding to each of the multiple graph nodes can be determined as the second contrast loss, which can further improve the prediction accuracy.

[0061] Furthermore, the heat balance model further includes a relationship mapping sub-model for learning the strong correlation mapping relationship between the data of different input parameters; the input parameters include boundary parameters and characteristic parameters;

[0062] It also includes: when receiving a data adjustment instruction for the input parameters, based on the data adjustment result of the input parameters and the strong correlation mapping relationship, determine whether the parameters that have not been adjusted need to be adjusted, and further determine the data of each input parameter input to the second prediction sub-model based on the determination result.

[0063] In the embodiments of the present invention, there is a strong correlation mapping relationship between data of different input parameters. That is to say, after a designer inputs data of boundary parameters and characteristic parameters for the first time, when it is necessary to adjust the data of a certain parameter, when viewing the adjusted heat balance diagram, due to the strong correlation mapping relationship between the data of different parameters, other parameters with the corresponding strong correlation mapping relationship also need to be adjusted adaptively. At this time, without the designer adjusting other parameters, the heat balance model can automatically determine whether the unadjusted parameters need to be adjusted based on the data adjustment result of the input parameters and the strong correlation mapping relationship. If the unadjusted parameters need to be adjusted, it further determines the adjusted data corresponding to the parameters that need to be adjusted, and further determines the data of each input parameter input into the second prediction sub-model based on the determination result, so that the second prediction sub-model can predict thermal parameters based on accurate characteristic parameter data, thereby further improving the prediction accuracy.

[0064] In the embodiments of the present invention, a heat balance model is obtained through training. Even when facing variable operating conditions of the energy system, it can adjust the input data of the heat balance model based on the characteristic parameters corresponding to the performance changes under different load conditions, so as to quickly obtain the corresponding heat balance diagram under variable operating conditions.

[0065] In one embodiment of the present invention, an interactive interface can also be provided. Designers can adjust the input data in real time and view the output heat balance diagram results, thereby realizing efficient, accurate, and flexible heat balance diagram calculation, and significantly improving the design efficiency and quality.

[0066] In one embodiment of the present invention, corresponding heat balance models can also be obtained through training for different energy systems respectively, and a guided interface is used to determine the target energy system input by the user. Then, the heat balance diagram model of the corresponding target energy system is called, and each boundary parameter and each characteristic parameter required for input are determined based on the called heat balance diagram model. When the user inputs corresponding data for the boundary parameters and characteristic parameters, the corresponding heat balance diagram is output using the heat balance diagram model.

[0067] The method for automatically generating a thermal balance diagram of an energy system provided by an embodiment of the present invention can reduce the dependence on the experience of designers, reduce human errors, significantly improve the calculation efficiency, shorten the design cycle, and reduce project costs; the thermal balance model can provide a more reliable theoretical basis for system design and optimization, and provide system performance and reliability. Moreover, the thermal balance model can be widely applied to different energy systems, such as thermal power, compressed air energy storage, gas-steam combined cycle, nuclear power generation, etc., and can realize the design of the system, improve the calculation analysis accuracy and speed of the energy system of actual engineering projects. In addition, it can also support the generation of thermal balance diagrams under variable operating conditions, respond to user inputs and system changes in real time, and is applicable to the analysis and optimization of dynamic processes such as system startup, shutdown, and load changes.

[0068] Please refer to Figure 3 , an embodiment of the present invention provides an automatic generation device for a thermal balance diagram of an energy system, and the device includes:

[0069] A construction unit 300, configured to construct a basic database based on historical project data of a target energy system; the basic database includes thermal balance diagrams of multiple types corresponding to the target energy system; the thermal balance diagram includes a graphic ontology and thermal parameters marked on graphic nodes, and the graphic ontology is formed by multiple graphic nodes and their connection relationships;

[0070] An acquisition unit 302, configured to acquire a plurality of training samples based on the basic database; the training samples include data of boundary parameters and characteristic parameters as inputs, and also include thermal balance diagrams as outputs;

[0071] A training unit 304, configured to train a thermal balance model by using a plurality of the training samples, so that the thermal balance model determines the graphic ontology of the thermal balance diagram to be output based on the data of the boundary parameters in the training samples, and predicts the thermal parameters of each graphic node in the graphic ontology based on the data of the boundary parameters and the data of the characteristic parameters in the training samples, and adjusts the model parameters based on the loss between the prediction result and the thermal parameters of the graphic nodes on the thermal balance diagram in the training samples to obtain a trained thermal balance model;

[0072] A thermal balance calculation unit 306, configured to output a thermal balance diagram for the input data to be generated for the thermal balance diagram by using the thermal balance model; the input data is input according to boundary parameters and characteristic parameters.

[0073] In an embodiment of the present invention, the thermal balance model at least includes: a first prediction sub-model and a second prediction sub-model; the first prediction sub-model is used to predict the graphic ontology of the thermal balance diagram to be output; the second prediction sub-model is used to predict the corresponding thermal parameters for the graphic nodes in the predicted graphic ontology output by the first prediction sub-model.

[0074] In an embodiment of the present invention, the first prediction sub-model and the second prediction sub-model are trained by a joint training method;

[0075] The joint training method includes:

[0076] For each training sample, the following operations are performed: input the data of the boundary parameters in the training sample into the first prediction sub-model, and obtain the predicted graphic ontology output by the first prediction sub-model; input the predicted graphic ontology, the data of the boundary parameters, and the data of the characteristic parameters in the training sample into the second prediction sub-model, so that the second prediction sub-model outputs the predicted thermal parameters corresponding to each graphic node in the predicted graphic ontology; jointly adjust the model parameters of the first prediction sub-model and the second prediction sub-model by using a first contrast loss and a second contrast loss; the first contrast loss is the contrast loss between the predicted graphic ontology and the graphic ontology of the thermal balance diagram in the training sample, and the second contrast loss is the contrast loss between the predicted thermal parameters and the thermal parameters of each graphic node on the thermal balance diagram in the training sample.

[0077] In an embodiment of the present invention, the calculation method of the second contrast loss includes: determining a parameter transfer relationship based on the connection relationship of the graphic nodes in the predicted graphic ontology; the parameter transfer relationship includes an energy transfer relationship, a mass transfer relationship, and a kinetic energy transfer relationship; outputting the predicted thermal parameters for each graphic node in sequence based on the parameter transfer relationship, and calculating a third contrast loss between the predicted thermal parameter of each graphic node and the thermal parameter in the training sample, and determining the sum of the third contrast losses corresponding to each of the multiple graphic nodes as the second contrast loss.

[0078] In an embodiment of the present invention, the thermal balance model further includes a relationship mapping sub-model for learning a strong correlation mapping relationship between data of different input parameters; the input parameters include boundary parameters and characteristic parameters;

[0079] The thermal balance calculation unit is further configured to: when receiving a data adjustment instruction for the input parameters, determine whether the parameters that have not been adjusted need to be adjusted based on the data adjustment result of the input parameters and the strong correlation mapping relationship, and further determine the data of each input parameter input into the second prediction sub-model based on the determination result.

[0080] In an embodiment of the present invention, the target energy system is at least one of the following: a thermal power generation system, a compressed air energy storage system, a gas-steam combined cycle system, and a nuclear power generation system.

[0081] It should be noted that: the automatic generation device of the energy system heat balance diagram provided in the above embodiments is only illustrated by dividing the above functional modules. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the automatic generation device of the energy system heat balance diagram provided in the above embodiments and the embodiments of the automatic generation method of the energy system heat balance diagram belong to the same concept. For the specific implementation process, please refer to the method embodiments and will not be elaborated here.

[0082] Embodiments of the present application also provide a computer device. Please refer to Figure 4 , which includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor to implement the automatic generation method of the energy system heat balance diagram provided in each of the above method embodiments.

[0083] Embodiments of the present application also provide a computer-readable storage medium, on which at least one instruction, at least one program, a code set or an instruction set is stored, and the at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor to implement the automatic generation method of the energy system heat balance diagram provided in each of the above method embodiments.

[0084] Embodiments of the present application also provide a computer program product, which includes a computer program. The processor of the computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the automatic generation method of the energy system heat balance diagram described in any one of the above embodiments.

[0085] For the convenience of description, when describing the above system or device, various modules or units are described separately according to functions. Of course, when implementing the present application, the functions of each unit can be implemented in one or more software and / or hardware.

[0086] From the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments of the present application.

[0087] Finally, it should also be noted that in this text, relational terms such as first, second, third, and fourth are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article, or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the said element.

[0088] The above are only the preferred embodiments of the present application. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A method for automatically generating a heat balance diagram of an energy system, characterized in that: The method includes: Based on the historical project data of the target energy system, a basic database is constructed; the basic database includes thermal balance diagrams of multiple types corresponding to the target energy system; the thermal balance diagram includes a graphic ontology and thermal parameters marked on the graphic nodes, and the graphic ontology is formed by multiple graphic nodes and their connection relationships; Based on the basic database, multiple training samples are obtained; the training samples include data of boundary parameters and characteristic parameters as inputs, and also include thermal balance diagrams as outputs; Using the multiple training samples to train a thermal balance model, so that the thermal balance model determines the graphic ontology of the thermal balance diagram to be output based on the data of the boundary parameters in the training samples, and predicts the thermal parameters of each graphic node in the graphic ontology based on the data of the boundary parameters and the data of the characteristic parameters in the training samples, and adjusts the model parameters based on the loss between the prediction result and the thermal parameters of the graphic nodes on the thermal balance diagram in the training samples to obtain a trained thermal balance model; Using the thermal balance model to output a thermal balance diagram for the input data of the thermal balance diagram to be generated; the input data is input according to the boundary parameters and the characteristic parameters.

2. The method according to claim 1, characterized in that: The thermal balance model at least includes: a first prediction sub-model and a second prediction sub-model; the first prediction sub-model is used to predict the graphic ontology of the thermal balance diagram to be output; the second prediction sub-model is used to predict the corresponding thermal parameters for the graphic nodes in the predicted graphic ontology output by the first prediction sub-model.

3. The method according to claim 2, characterized in that, The first prediction sub-model and the second prediction sub-model are trained by an integrated training method; The integrated training method includes: For each training sample, perform: input the data of the boundary parameters in the training sample into the first prediction sub-model, and obtain the predicted graphic ontology output by the first prediction sub-model; input the predicted graphic ontology, the data of the boundary parameters, and the data of the characteristic parameters in the training sample into the second prediction sub-model, so that the second prediction sub-model outputs the predicted thermal parameters corresponding to each graphic node in the predicted graphic ontology; use the first contrast loss and the second contrast loss to jointly adjust the model parameters of the first prediction sub-model and the second prediction sub-model; the first contrast loss is the contrast loss between the predicted graphic ontology and the graphic ontology of the thermal balance diagram in the training sample, and the second contrast loss is the contrast loss between the predicted thermal parameters and the thermal parameters of each graphic node on the thermal balance diagram in the training sample.

4. The method according to claim 3, wherein The calculation method of the second contrast loss includes: Determine the parameter transfer relationship based on the connection relationship of the graphic nodes in the predicted graphic ontology; the parameter transfer relationship includes energy transfer relationship, mass transfer relationship, and kinetic energy transfer relationship; Based on the parameter transfer relationship, sequentially output the predicted thermal parameters for each graphic node, calculate the third contrast loss between the predicted thermal parameters of each graphic node and the thermal parameters in the training sample, and determine the sum of the third contrast losses corresponding to the multiple graphic nodes one by one as the second contrast loss.

5. The method according to claim 3, characterized in that: The thermal balance model also includes a relationship mapping sub-model for learning a strong correlation mapping relationship between data of different input parameters; the input parameters include boundary parameters and characteristic parameters; It also includes: when a data adjustment instruction of an input parameter is received, based on the data adjustment result of the input parameter and the strong correlation mapping relationship, determining whether the unadjusted parameter needs data adjustment, and further determining the data input to each input parameter in the second prediction sub-model based on the determination result.

6. The method according to any one of claims 1-5, characterized in that, The target energy system is at least one of the following: a thermal power generation system, a compressed air energy storage system, a gas-steam combined cycle system, and a nuclear power generation system.

7. An automatic generation device for a heat balance diagram of an energy system, characterized in that: The device comprises: A construction unit is configured to construct a basic database based on historical project data of a target energy system; the basic database includes multiple types of heat balance diagrams corresponding to the target energy system; the heat balance diagram includes a graph body and thermal parameters marked on the graph nodes, the graph body being formed by multiple graph nodes and their connection relationships; an acquisition unit, configured to acquire a plurality of training samples based on the basic database; the training samples include data of boundary parameters and characteristic parameters as input, and also include a thermal balance diagram as output; a training unit configured to train a thermal balance model using the plurality of training samples, so that the thermal balance model determines a graph body of a thermal balance diagram to be output based on the data of boundary parameters in the training samples, predicts the thermal parameters of each graph node in the graph body based on the data of boundary parameters and characteristic parameters in the training samples, and adjusts model parameters based on a loss between the prediction results and the thermal parameters of the graph nodes on the thermal balance diagram in the training samples, thereby obtaining a trained thermal balance model; The heat balance calculation unit is used to output a heat balance diagram according to the input data of the heat balance diagram to be generated by using the heat balance model; the input data is input according to boundary parameters and characteristic parameters.

8. A computer device, characterized in that, The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to implement the steps of any one of the methods described in claims 1-6.

9. A computer-readable storage medium, characterized in that The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises a computer program, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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