State identification method and device for steam power system

Abstract

The invention provides a state recognition method and device for a steam power system. The state recognition method comprises the steps that operation parameters of the steam power system are obtained; according to the operation parameters and a pre-established fluid network model, the operation state corresponding to the operation parameters of the steam power system is determined, the pre-established fluid network model is obtained by weighting a mechanism analysis sub-model and a data driving sub-model, the advantages of traditional mechanism modeling are fully utilized, and the system performance is improved. The physical significance of the model structure is reserved; test and operation data under various working conditions are fully mined and utilized, the approximation degree of the key working conditions of the system is continuously improved through training, and reliable model input is provided for system optimization. The established model is high in precision, strong in adaptability and wide in coverage range, and the established hybrid model can better combine the real-time operation state of the system to realize the real-time accurate representation of the system state.

Description

Technical Field

[0001] The present invention belongs to the technical field of steam power, and particularly relates to a method and device for identifying the state of a steam power system. Background Art

[0002] As the key to energy conversion, the steam power system undertakes important tasks such as steam transportation and work done, and completing the steam-water cycle, which is crucial for the safe and stable operation of the system. The equipment configuration of the steam power system is complex, and the system exhibits strong nonlinear coupling dynamic characteristics. Therefore, it is difficult to establish a high-precision dynamic characteristic model of the steam power system, but it is crucial for system characteristic analysis and subsequent control strategy formulation.

[0003] At present, the construction of the dynamic characteristic model of the steam power system is mainly divided into two types: mechanism modeling and data-driven modeling. The former is constructed based on mechanism analysis, starting from the internal mechanism of the system, and establishing a theoretical analytical model according to the law of conservation of mass and energy. The latter is to establish a data-driven model based on the input-output relationship of actual operation data. The mechanism modeling has clear physical meaning, but in the face of the complex operation mechanism of the thermal system, the process is complex and the accuracy is difficult to guarantee. The data-driven modeling is simple and fast, but the interpretability of the model is not strong, it cannot well reflect the production process mechanism and depends on the data reliability, and the training data limits the modeling ability. Therefore, how to provide a method for identifying the state of the steam power system has become a technical problem that needs to be solved urgently in this field. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and device for identifying the state of a steam power system.

[0005] According to the first aspect of the present invention, a method for identifying the state of a steam power system is provided, including,

[0006] Obtaining the operation parameters of the steam power system;

[0007] Determining the operating state corresponding to the operation parameters of the steam power system according to the operation parameters and a pre-established fluid network model, wherein the pre-established fluid network model is obtained by weighting a mechanism analysis sub-model and a data-driven sub-model.

[0008] Optionally, the pre-established fluid network model is obtained by the following method:

[0009] Obtaining historical operation data;

[0010] Preprocessing the historical operation data to obtain processed historical operation data;

[0011] Performing condition clustering processing on the processed historical operation data to obtain a fluid network model.

[0012] Optionally, performing operating condition clustering on the processed historical operation data to obtain a fluid network model, including:

[0013] Using the K-Means clustering algorithm to cluster the processed historical operation data, clustering and dividing the stable operating condition parameter set, and saving the optimal clustering center and the clustering samples under each stable operating condition;

[0014] Outputting the clustering center and the clustering samples to the mechanism analysis sub-model for operating condition division;

[0015] For each cluster, using the particle swarm optimization algorithm to perform parallel optimization calculation to determine the optimal set of parameters to be identified, performing curve fitting on multiple parameter sets, and fitting multiple models into a clustering model applicable to the full operating conditions;

[0016] Determining the clustering model as the fluid network model.

[0017] Optionally, preprocessing the historical operation data to obtain processed historical operation data, including:

[0018] Performing data integration, data cleaning, and data conversion processing on the historical operation data to obtain processed historical operation data.

[0019] Optionally, the fluid network model at least includes models of various different thermal equipment, and the model of each thermal equipment complies with the mass conservation, momentum conservation, and energy conservation relationships.

[0020] According to the second aspect of the present invention, there is provided a state recognition device for a steam power system, including:

[0021] An acquisition module for acquiring the operation parameters of the steam power system;

[0022] A determination module for determining the operating state corresponding to the operation parameters of the steam power system according to the operation parameters and a pre-established fluid network model, wherein the pre-established fluid network model is obtained by weighting a mechanism analysis sub-model and a data-driven sub-model.

[0023] Optionally, the device further includes a model establishment module, and the model establishment module is used for:

[0024] Acquiring historical operation data;

[0025] Preprocessing the historical operation data to obtain processed historical operation data;

[0026] Performing operating condition clustering on the processed historical operation data to obtain a fluid network model.

[0027] Optionally, the model building is used for:

[0028] Using the K-Means clustering algorithm to cluster the processed historical operation data, clustering and dividing the stable operating condition parameter set, and saving the optimal clustering center and the clustering samples under each stable operating condition;

[0029] Outputting the clustering center and the clustering samples to the mechanism analysis sub-model for operating condition division;

[0030] For each cluster, using the particle swarm optimization algorithm to perform parallel optimization calculation to determine the optimal set of parameters to be identified, performing curve fitting on multiple parameter sets, and fitting multiple models into a clustering model applicable to the full operating conditions;

[0031] Determining the clustering model as the fluid network model.

[0032] Optionally, the model building is used for:

[0033] Performing data integration, data cleaning, and data transformation on the historical operation data to obtain the processed historical operation data.

[0034] Optionally, the fluid network model at least includes models of various different thermal equipment, and the model of each thermal equipment complies with the mass conservation, momentum conservation, and energy conservation relationships.

[0035] In a third aspect, the present application discloses an electronic device, which includes: a processor; a memory for storing processor-executable instructions; wherein, the processor is configured to execute the method described in any of the above aspects.

[0036] In a fourth aspect, the present application discloses a non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by a processor of an electronic device, enabling the electronic device to execute the method described in any of the above aspects.

[0037] In a fifth aspect, the present application discloses a computer program product, when the instructions in the computer program product are executed by a processor of an electronic device, enabling the electronic device to execute the method described in any of the above aspects.

[0038] The beneficial effects brought by the present invention are as follows:

[0039] As can be seen from the above solution, the embodiments of the present invention provide a method and device for identifying the state of a steam power system, including: obtaining the operating parameters of the steam power system; determining the operating state corresponding to the operating parameters of the steam power system according to the operating parameters and a pre-established fluid network model, where the pre-established fluid network model is obtained by weighting a mechanism analysis sub-model and a data-driven sub-model, making full use of the advantages of traditional mechanism modeling and retaining the physical meaning of the model structure; fully mining and utilizing test and operation data under various working conditions, continuously improving the approximation degree of the key working conditions of the system through training, and providing reliable model inputs for system optimization. The established model has high accuracy, strong adaptability and wide coverage, and the established hybrid model can better combine the real-time operating state of the system to achieve real-time and accurate characterization of the system state. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 FIG. is a schematic flow chart of a method for identifying the state of a steam power system according to an embodiment;

[0041] Figure 2 FIG. is a schematic flow chart of another method for identifying the state of a steam power system according to an embodiment;

[0042] Figure 3 FIG. is a schematic flow chart of a data preprocessing according to an embodiment;

[0043] Figure 4 is a structural block diagram of a device for identifying the state of a steam power system of the present application;

[0044] Figure 5 is a block diagram of an electronic device of the present application;

[0045] Figure 6 is a block diagram of a computer-readable storage medium of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying 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 shall fall within the protection scope of the present invention.

[0047] Referring to Figure 1 , a step flow chart of a method for identifying the state of a steam power system of the present application is shown. This method can be applied to an electronic device. Specifically, the method can include the following steps:

[0048] S101. Obtain the operating parameters of the steam power system;

[0049] S102. Determine the operating state corresponding to the operating parameters of the steam power system according to the operating parameters and the pre-established fluid network model, wherein the pre-established fluid network model is obtained by weighting the mechanism analysis sub-model and the data-driven sub-model.

[0050] Another embodiment of the present application further supplements and explains the method for identifying the state of the steam power system provided in the above embodiment.

[0051] To make the established model better reflect the dynamic characteristics of the system and meet the requirements of real-time performance and accuracy of operation optimization, the embodiment of the present application combines mechanism modeling and data-driven modeling to propose a hybrid-driven modeling method for steam power systems. Among them, the mechanism model provides prior knowledge for the data-driven model, which can make full use of the advantages of traditional mechanism modeling and retain the physical meaning of the model structure; while based on data-driven, various test and operation data under various working conditions can be fully mined and utilized, and the approximation degree of the key working conditions of the system can be continuously improved through training, providing reliable model inputs for system optimization. Based on this, a steam power system model with high accuracy, strong adaptability and wide coverage can be established.

[0052] As the key to energy conversion, the steam power system is crucial for safe and stable operation. At present, the widely used modeling methods based on pure mechanism or pure data for steam power systems are difficult to meet the requirements of real-time performance and accuracy of operation optimization. The main technical problem to be solved is the wide working condition modeling technology for non-linear strongly coupled systems.

[0053] The present invention is a modeling method for steam power systems based on mechanism + data hybrid drive, and has the following beneficial effects:

[0054] Make full use of the advantages of traditional mechanism modeling and retain the physical meaning of the model structure; fully mine and utilize various test and operation data under various working conditions, and continuously improve the approximation degree of the key working conditions of the system through training, providing reliable model inputs for system optimization. The established model has high accuracy, strong adaptability and wide coverage.

[0055] The adopted model parameter online calibration method and model weighted fusion method give full play to the advantages of different modeling methods. The established hybrid model can better combine the real-time operating state of the system and realize the real-time and accurate characterization of the system state.

[0056] Facing the complex and changeable thermal system, uncertain external environment, and strong coupling effect between different types of variables, if the established model wants to accurately reflect the true characteristics of the system, it is necessary to combine the dynamic characteristics of different working conditions to improve the accuracy of the model.

[0057] Optionally, a pre-established fluid network model is obtained by the following method:

[0058] Obtain historical operation data;

[0059] Preprocess the historical operation data to obtain preprocessed historical operation data;

[0060] Perform operating condition clustering on the preprocessed historical operation data to obtain a fluid network model.

[0061] Optionally, performing operating condition clustering on the preprocessed historical operation data to obtain a fluid network model includes:

[0062] Use the K-Means clustering algorithm to cluster the preprocessed historical operation data, cluster and divide the stable operating condition parameter set, and save the optimal clustering center and clustering samples under each stable operating condition;

[0063] Output the clustering center and clustering samples to the mechanism analysis sub-model for operating condition division;

[0064] For each cluster, use the particle swarm optimization algorithm to perform parallel optimization calculation to determine the optimal set of parameters to be identified, perform curve fitting on multiple parameter sets, and fit multiple models into a clustering model applicable to the full operating conditions;

[0065] Determine the clustering model as the fluid network model.

[0066] Optionally, preprocessing the historical operation data to obtain preprocessed historical operation data includes:

[0067] Perform data integration, data cleaning, and data conversion on the historical operation data to obtain preprocessed historical operation data.

[0068] As Figure 3 shown, the present invention is implemented through the following technical route. First, in combination with the attached Figure 2 Introduce the data preprocessing scheme. Data preprocessing is a very necessary link in the data mining process and provides high-quality and highly reliable data input for modeling. Data preprocessing includes data integration, data cleaning, data conversion, etc. Data integration mainly includes unifying and concentrating interrelated data with heterogeneity from different sources to ensure data consistency. Data cleaning mainly performs data filling, direct elimination, etc. on the null values, error values, noise values, etc. of the data through preset algorithms. Data conversion refers to standardizing linear or non-linear data to eliminate differences in noise, attributes, precision, different dimensions, etc. in the data, facilitating data modeling.

[0069] Optionally, the fluid network model at least includes models of various different thermal devices, and the model of each thermal device complies with the mass conservation, momentum conservation, and energy conservation relationships.

[0070] As Figure 2 shown, the present invention can be generally divided into the following key steps:

[0071] 1) Based on mechanism analysis and modular modeling, construct a fluid network model of the thermal system

[0072] Based on the structural analysis of the thermal system of the steam power system, using the idea of modular modeling, model the main thermal devices in the thermal system of the steam power system: control valves, pumps, heat exchangers, etc. The module division takes independent thermal devices as the basic modules, and mathematically, the independence and compatibility between modules can be guaranteed. Mechanistically, the model strictly complies with the laws of mass and energy conservation, and the constructed model covers the full operating conditions of the studied thermal system. Taking the centrifugal water pump model and the heat exchanger model as examples:

[0073] For the mechanism modeling of the centrifugal water pump, first fill in the model constants according to the equipment design data to simulate its working characteristics. Then use the on-site measured data, mainly including the measured flow rate, inlet and outlet pressures, inlet temperature, measured torque, and pump speed of the water pump, etc., to correct the model characteristic parameters, so that the regulation characteristics are consistent with the actual characteristics, achieving the effect of synchronizing the centrifugal water pump model with the actual operation.

[0074] Assume that the number of blades is infinite and infinitely thin. For an ideal fluid, according to the moment of momentum theorem and the energy conservation equation, the energy obtained when passing through the impeller is H T∞ , and its calculation is shown in Equation (6-1).

[0075]

[0076] In the formula, g is the acceleration due to gravity; u 2 is the outlet circumferential velocity; v 2m∞ is the outlet absolute velocity; β 2a is the outlet flow angle; D 2 is the outlet impeller diameter; b 2 is the outlet blade width; q vt is the theoretical flow rate passing through the impeller. If the dimensions and rotational speed of the pump and fan have been obtained, the above formula is a linear equation in which H T∞ varies linearly with q vt .

[0077] The above analysis is all based on the ideal situation of the infinite blade assumption. For an actual pump with finite blades in operation, the actual head needs to be corrected, H T = K T H T∞ , where KT is the head correction coefficient.

[0078] For the mechanism modeling of the heat exchanger, the continuous process of simultaneous heat release and heat absorption is decomposed into two sub-processes of heat absorption and heat release on the tube wall that alternate with each other, and the calculations of each sub-process are independent of each other.

[0079] According to the established heat exchanger model, by filling in parameters such as the heat transfer area, the heat transfer coefficient and the resistance characteristics can be accurately calculated; then, the fouling coefficient of the heat exchanger is corrected using real-time data, and parameters such as the temperature change rate of the heat exchanger are corrected using the real-time flow rate.

[0080] The heat transfer cross-sectional area can be calculated based on the rated flow rate, rated flow velocity, outlet pressure and temperature. The calculation of the heat transfer quantity is shown in Equation (6-2).

[0081] Q = KS a ΔT = GΔh (6-2)

[0082] In the formula, Q is the heat transfer quantity; K is the total heat transfer coefficient; S a is the heat transfer cross-sectional area; ΔT is the heat transfer temperature difference; G is the mass flow rate; Δh is the change in specific enthalpy.

[0083] The calculation of the total heat transfer coefficient K is shown in Equation (6-3).

[0084]

[0085] In the formula, the key parameter of heat transfer calculation by the model, the convective heat transfer coefficient α 1 , is calculated based on the structural design parameters of the heat exchanger such as the cross-sectional area, etc. K th is the tube wall thickness, λ is the thermal conductivity, which can be obtained according to the design parameters, K foul is the fouling coefficient, which can be corrected in real time according to the operation data.

[0086] In the thermal system, fluids such as steam and water are distributed and converged through different pipelines to form a complex fluid network. According to the calculation requirements, a sufficient number of control volumes are selected in the fluid network system, and the mass, momentum and energy conservation equations are established. Together with the equipment characteristic equations, the pressure, flow rate and temperature distribution of the entire thermal pipeline network can be calculated.

[0087] Each modular model consists of sub-modules that describe the mass conservation, momentum conservation and energy conservation relationships of the system and several equipment modules that describe the thermodynamic characteristics of the equipment, and are interconnected using topological parameters. Thus, a fluid network model of the thermal system is formed.

[0088] 2) Parameter identification based on data-driven and construction of full-condition clustering model

[0089] There are a large number of parameters to be determined in the mechanism model established according to the system principle. At present, they are mainly determined by empirical data or offline operation data. There is a lack of online calibration mechanism for model parameters, which greatly reduces the prediction accuracy when the working conditions change. Therefore, the system mechanism characteristics and data characteristics are fully integrated, and adaptive parameter correction based on intelligent optimization and online fusion of clustering models based on data drive are proposed.

[0090] First, the real-time dynamic data of the on-site PLC or DCS is read online and used as the input of the mechanism model and the data-driven model; the ability of the model to track the actual system is judged by comparing the similarity between the actual on-site output and the model output; when the model cannot track the actual system well, the extracted characteristic parameters are used as input, and the adaptive parameter online correction method based on the intelligent optimization algorithm is used to perform parameterized calculations on the model-related parameters, obtain new parameter values, and complete the update of the model parameters;

[0091] 3) Weighted fusion based on mechanism + data-driven model

[0092] In order to further improve the accuracy and robustness of the model and give full play to the advantages of different modeling methods, we can choose to effectively integrate the modeling processes or prediction results of different models.

[0093] Based on information entropy theory, for the prediction results of different models, if the relative prediction error is larger, the corresponding weight should be set smaller when the model is fused.

[0094] For the two modeling methods that need to be integrated in the present invention, the relative error of the model prediction at time t can be expressed as e 1t 、e 2t . Calculate the relative prediction error of each model in the N sequence, and then perform standardization to obtain p 1t 、p 2t .

[0096]

[0097] Calculate the information entropy h of the relative error of the two methods i , the calculation is shown in formula (6-5).

[0098]

[0099] The weight coefficient w to be fused i It can be calculated by formula (6-6).

[0100] In view of the nonlinear and large hysteresis characteristics of thermal systems under variable operating conditions, a clustering model applicable to the entire operating range is proposed to improve the modeling accuracy of the model under different operating conditions.

[0101] Step 1: First, the model parameters to be identified are grouped into one or more sets of parameters to be identified.

[0102] Step 2: Extract the input-output characteristic data from the historical operation database. Use the K-Means clustering algorithm for clustering, cluster and divide the stable operating condition parameter sets, and save the optimal clustering centers and the clustering samples under each stable operating condition.

[0103] Step 3: Output the clustering centers and clustering samples to the mechanism model for operating condition division. For each cluster, use classic intelligent optimization algorithms such as particle swarm to perform parallel optimization calculations to determine the optimal set of parameters to be identified, perform curve fitting on multiple parameter sets, and fit multiple models into a clustering model applicable to the full operating conditions.

[0104]

[0105] Based on the weighted fusion of the mechanism + data-driven model, to a certain extent, it fully integrates the system mechanism characteristics and operation data characteristics, and gives full play to the advantages of different modeling methods. The established hybrid model can better combine the real-time operation state of the system and achieve real-time and accurate characterization of the system state.

[0106] The present invention proposes a modeling method for a steam power system based on mechanism + data hybrid drive. The scheme features are as follows:

[0107] 1) Based on the thermodynamic characteristic analysis of the steam power system, adopt the idea of modular modeling to model the main thermal equipment in the steam power system. Combine with online reading of on-site real-time dynamic data to identify and adaptively correct the relevant parameters of the model.

[0108] 2) Facing the nonlinear and large lag characteristics of the thermal system under variable operating conditions, a clustering model applicable to the full operating condition range is proposed to improve the modeling accuracy of the model under different operating conditions.

[0109] 3) Effectively fuse different model modeling processes or prediction results through weight coefficients. When the parameterization method cannot complete model synchronization, the model fusion technology is used to complete the update of the model. Give full play to the advantages of different modeling methods and further improve the accuracy and robustness of the model.

[0110] The embodiments of the present invention provide a method and a device for identifying the state of a steam power system, including: obtaining the operating parameters of the steam power system; determining the operating state corresponding to the operating parameters of the steam power system according to the operating parameters and a pre-established fluid network model, wherein the pre-established fluid network model is obtained by weighting a mechanism analysis sub-model and a data-driven sub-model, making full use of the advantages of traditional mechanism modeling and retaining the physical meaning of the model structure; fully excavating and utilizing the test and operating data under various working conditions, continuously improving the approximation degree of the key working conditions of the system through training, and providing reliable model inputs for system optimization. The established model has high accuracy, strong adaptability and wide coverage, and the established hybrid model can better combine the real-time operating state of the system to realize the real-time and accurate characterization of the system state.

[0111] It should be noted that each implementable manner in this embodiment can be implemented independently, or can be combined in any combination manner without conflict. The present application does not make a limitation.

[0112] Another embodiment of the present application provides a device for identifying the state of a steam power system, which is used to execute the method for identifying the state of the steam power system provided in the above embodiment.

[0113] As Figure 4 shown, it is a schematic structural diagram of the device for identifying the state of the steam power system provided in the embodiment of the present application. The xx device includes an acquisition module 401 and a determination module 402, wherein:

[0114] The acquisition module 401 is used to obtain the operating parameters of the steam power system;

[0115] The determination module 402 is used to determine the operating state corresponding to the operating parameters of the steam power system according to the operating parameters and a pre-established fluid network model, wherein the pre-established fluid network model is obtained by weighting a mechanism analysis sub-model and a data-driven sub-model.

[0116] Regarding the device in this embodiment, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0117] Another embodiment of the present application further supplements the device for identifying the state of the steam power system provided in the above embodiment.

[0118] Optionally, the device further includes a model establishment module, and the model establishment module is used for:

[0119] Obtaining historical operation data;

[0120] Preprocessing the historical operation data to obtain the preprocessed historical operation data;

[0121] Perform operating condition clustering on the processed historical operation data to obtain a fluid network model.

[0122] Optionally, the model is established for:

[0123] Use the K-Means clustering algorithm to cluster the processed historical operation data, cluster and divide the stable operating condition parameter set, and save the optimal clustering center and the clustering samples under each stable operating condition;

[0124] Output the clustering center and clustering samples to the mechanism analysis sub-model for operating condition division;

[0125] For each cluster, use the particle swarm optimization algorithm to perform parallel optimization calculation to determine the optimal set of parameters to be identified, perform curve fitting on multiple parameter sets, and fit multiple models into a clustering model applicable to the full operating conditions;

[0126] Determine the clustering model as the fluid network model.

[0127] Optionally, the model is established for:

[0128] Perform data integration, data cleaning, and data conversion on the historical operation data to obtain the processed historical operation data.

[0129] Optionally, the fluid network model at least includes models of various different thermal equipment, and the model of each thermal equipment complies with the mass conservation, momentum conservation, and energy conservation relationships.

[0130] The embodiments of the present invention provide a method and device for state recognition of a steam power system, including: obtaining the operation parameters of the steam power system; determining the operating state corresponding to the operation parameters of the steam power system according to the operation parameters and a pre-established fluid network model, wherein the pre-established fluid network model is obtained by weighting a mechanism analysis sub-model and a data-driven sub-model, making full use of the advantages of traditional mechanism modeling and retaining the physical meaning of the model structure; fully excavating and utilizing the test and operation data under various operating conditions, continuously improving the approximation degree of the key operating conditions of the system through training, and providing reliable model inputs for system optimization. The established model has high accuracy, strong adaptability, and wide coverage, and the established hybrid model can better combine the real-time operating state of the system to achieve real-time and accurate characterization of the system state.

[0131] For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for the related parts, refer to the partial description of the method embodiments.

[0132] Optionally, an embodiment of the present application further provides an electronic device, including: a processor, a memory, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements each process of the above method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described here again.

[0133] An embodiment of the present application further provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, it implements each process of the above method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described here again. Among them, the computer-readable storage medium is, for example, a read-only memory (ROM for short), a random access memory (RAM for short), a magnetic disk, or an optical disc, etc.

[0134] Figure 5 FIG. 800 is a block diagram of an electronic device 800 shown in the present application. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0135] Refer to Figure 5 , the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0136] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, telephone call, data communication, camera operation, and recording operation. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0137] The memory 804 is configured to store various types of data to support the operation of the device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, images, videos, and the like. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk.

[0138] The power supply component 806 provides power to various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.

[0139] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can not only sense the boundaries of the touch or swipe actions but also detect the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.

[0140] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC) that is configured to receive external audio signals when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 further includes a speaker for outputting audio signals.

[0141] The I / O interface 812 provides an interface between the processing component 802 and a peripheral interface module, which can be a keyboard, a click wheel, buttons, etc. These buttons can include, but are not limited to: a home button, a volume button, a power-on button, and a lock button.

[0142] The sensor assembly 814 includes one or more sensors for providing a status assessment of various aspects of the electronic device 800. For example, the sensor assembly 814 can detect the on / off state of the device 800, the relative positioning of components, such as the display and keypad of the electronic device 800. The sensor assembly 814 can also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. The sensor assembly 814 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 can also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 can further include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0143] The communication component 816 is configured to facilitate communication between the electronic device 800 and other devices in a wired or wireless manner. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G, or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast operation information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0144] In an exemplary embodiment, the electronic device 800 can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above method.

[0145] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as the memory 804 including instructions, and the above instructions can be executed by the processor 820 of the electronic device 800 to complete the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0146] Figure 6FIG. 0 is a block diagram of a computer-readable storage medium 1900 shown in the present application. For example, the computer-readable storage medium 1900 may be provided as a server.

[0147] Referring Figure 6 to FIG. 5, the computer-readable storage medium 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by a memory 1932 for storing instructions executable by the processing component 1922, such as application programs. The application programs stored in the memory 1932 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0148] The computer-readable storage medium 1900 may further include a power component 1926 configured to perform power management of the computer-readable storage medium 1900, a wired or wireless network interface 1950 configured to connect the computer-readable storage medium 1900 to a network, and an input / output (I / O) interface 1958. The computer-readable storage medium 1900 may operate based on an operating system stored in the memory 1932, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.

[0149] It should be noted that in this document, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device including a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the presence of additional identical elements in the process, method, article or device including the element.

[0150] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example method can be implemented by means of software plus a necessary general hardware platform, and of course also by hardware, but in many cases the former is a better implementation. 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, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in various embodiments of the present application.

[0151] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them fall within the protection scope of the present application.

[0152] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the embodiments of the present application can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0153] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0154] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling, direct coupling, or communication connection can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0155] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0156] In addition, the functional units in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0157] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0158] As described above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0159] The above is the preferred implementation manner of the present invention. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for identifying the state of a steam power system, characterized in that: include: Obtaining the operating parameters of the steam power system; According to the operating parameters and a pre-established fluid network model, an operating state corresponding to the operating parameters of the steam power system is determined, wherein the pre-established fluid network model is obtained by weighting a mechanism analysis sub-model and a data-driven sub-model.

2. The method for identifying the state of a steam power system according to claim 1, characterized in that: The pre-established fluid network model is obtained in the following manner: Get historical operation data; Preprocessing the historical operation data to obtain processed historical operation data; The processed historical operation data is subjected to operating condition clustering processing to obtain a fluid network model.

3. The method for identifying the state of a steam power system according to claim 2, characterized in that: The processing of operating condition clustering on the processed historical operation data to obtain a fluid network model includes: Use K-Means clustering algorithm to cluster the processed historical operation data, cluster the stable operating condition parameter set, and save the optimal cluster center and cluster samples under each stable operating condition; Output the cluster centers and cluster samples to the mechanism analysis sub-model for working condition division; For each cluster, the particle swarm optimization algorithm is used to perform parallel optimization calculations to determine the optimal set of parameters to be identified, and multiple parameter sets are curve fitted to fit multiple models into a clustering model suitable for all working conditions; The clustering model is determined as the fluid network model.

4. The method for identifying the state of a steam power system according to claim 2, characterized in that: The preprocessing of the historical operation data to obtain the processed historical operation data includes: The historical operation data is processed by data integration, data cleaning and data conversion to obtain processed historical operation data.

5. The method for identifying the state of a steam power system according to claim 1, characterized in that: The fluid network model at least includes models of various thermal devices, and the model of each thermal device complies with the relationship of conservation of mass, conservation of momentum and conservation of energy.

6. A state recognition device for a steam power system, characterized in that: include: An acquisition module, used for acquiring operating parameters of a steam power system; A determination module is used to determine the operating state corresponding to the operating parameters of the steam power system based on the operating parameters and a pre-established fluid network model, wherein the pre-established fluid network model is obtained by weighting a mechanism analysis sub-model and a data-driven sub-model.

7. The state identification device of the steam power system according to claim 6, characterized in that: The apparatus further comprises establishing a model, wherein the model is used to: Get historical operation data; Preprocessing the historical operation data to obtain processed historical operation data; The processed historical operation data is subjected to operating condition clustering processing to obtain a fluid network model.

8. The state identification device of the steam power system according to claim 7, characterized in that: The model is used to: Use K-Means clustering algorithm to cluster the processed historical operation data, cluster the stable operating condition parameter set, and save the optimal cluster center and cluster samples under each stable operating condition; Output the cluster centers and cluster samples to the mechanism analysis sub-model for working condition division; For each cluster, the particle swarm optimization algorithm is used to perform parallel optimization calculations to determine the optimal set of parameters to be identified, and multiple parameter sets are curve fitted to fit multiple models into a clustering model suitable for all working conditions; The clustering model is determined as the fluid network model.

9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the method according to any one of claims 1 to 5 when executed by the processor.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

Images