Method and device for monitoring carbon monoxide content in flue gas of gas turbine

Abstract

The present invention is applicable to the field of gas-fired distributed energy and provides a method and device for monitoring the flue gas carbon monoxide content of a gas turbine. The method comprises: obtaining a flue gas carbon monoxide content monitoring model request and operating data of a target gas turbine within an energy station to be monitored, wherein the flue gas carbon monoxide content monitoring model request carries a gas turbine identifier; determining a target joint energy station based on the gas turbine identifier; constructing a joint flue gas carbon monoxide content monitoring model based on the operating data of the gas turbines corresponding to each of the target joint energy stations; and generating the flue gas carbon monoxide content of the target gas turbine based on the joint flue gas carbon monoxide content monitoring model and the operating data of the target gas turbine. This embodiment achieves the goal of measuring exhaust gas oxygen content without the use of a sensor, enabling real-time monitoring of exhaust gas oxygen content, facilitating maintenance, and reducing measurement errors. Furthermore, the joint exhaust gas oxygen content measurement model relies on real data and is not easily affected by the external environment, allowing for relatively accurate determination of exhaust gas oxygen content.

Description

Technical Field

[0001] The present invention belongs to the field of gas-fired distributed energy, and in particular relates to a method and device for monitoring the carbon monoxide content in flue gas of a gas turbine. Background Art

[0002] Flue gas carbon monoxide content refers to the amount of carbon monoxide in the flue gas emitted after fuel combustion. It is a key indicator of gas turbine combustion, and its value is dependent on factors such as the turbine structure, fuel type and properties, load level, operating air supply conditions, and equipment sealing conditions. During actual gas turbine operation, to ensure complete fuel combustion, the actual air supply must be significantly greater than the theoretical air supply. This excess air supply is commonly referred to as excess air. The excess air coefficient is the ratio of the actual air supply to the theoretical air supply. Excessively low flue gas carbon monoxide content (i.e., a smaller excess air coefficient) results in insufficient oxygen for combustion, incomplete fuel combustion, and increased heat losses. Excessively high flue gas carbon monoxide content (i.e., a larger excess air coefficient) reduces the thermal efficiency of the gas turbine, impairing combustion and potentially exceeding environmental pollutant emissions standards. This also increases exhaust power consumption. Therefore, controlling the carbon monoxide content in the flue gas of the gas turbine within a reasonable range is of great significance for saving energy, maintaining the economical combustion of the gas turbine, and achieving safe, efficient and low-pollution emissions. In other words, the carbon monoxide content in the flue gas of the gas turbine is one of the important indicators to measure whether the gas turbine is operating safely, economically and environmentally friendly.

[0003] Currently, zirconium oxide sensors are generally used in industry to measure the carbon monoxide content in flue gas.

[0004] However, this type of sensor has many disadvantages, such as high cost, large measurement lag, difficult maintenance, large measurement error, and short life. It is not suitable for long-term use in scenarios where distributed energy is mainly based on small and medium-sized gas turbines. Summary of the Invention

[0005] In view of this, an embodiment of the present invention provides a method and device for monitoring the carbon monoxide content in flue gas of a gas turbine, which only requires a small amount of label data, can overcome the shortcomings of the sensor's complete actual measurement, and does not need to rely on a large number of physical parameters like physical modeling.

[0006] A first aspect of an embodiment of the present invention provides a method for monitoring the carbon monoxide content in the flue gas of a gas turbine, comprising: obtaining a flue gas carbon monoxide content monitoring model request of a target gas turbine in an energy station to be monitored and operating data of the target gas turbine, the flue gas carbon monoxide content monitoring model request carrying a gas turbine identifier; determining a target joint energy station based on the gas turbine identifier; constructing a joint flue gas carbon monoxide content monitoring model based on the operating data of the gas turbines corresponding to each of the target joint energy stations; generating the flue gas carbon monoxide content of the target gas turbine based on the joint flue gas carbon monoxide content monitoring model and the operating data of the target gas turbine.

[0007] A second aspect of an embodiment of the present invention provides a device for monitoring the carbon monoxide content in flue gas of a gas turbine, comprising: an acquisition module, configured to acquire a flue gas carbon monoxide content monitoring model request of a target gas turbine in an energy station to be monitored and operating data of the target gas turbine, wherein the flue gas carbon monoxide content monitoring model request carries a gas turbine identifier; a determination module, configured to determine a target joint energy station based on the gas turbine identifier; a construction module, configured to construct a joint flue gas carbon monoxide content monitoring model based on the operating data of the gas turbines corresponding to each of the target joint energy stations; and a generation module, configured to generate the flue gas carbon monoxide content of the target gas turbine based on the joint flue gas carbon monoxide content monitoring model and the operating data of the target gas turbine.

[0008] A third aspect of an embodiment of the present invention provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the computer program.

[0009] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

[0010] Compared with the prior art, the embodiments of the present invention have the following advantages: first, a flue gas carbon monoxide content monitoring model request of a target gas turbine in an energy station to be monitored and the operating data of the target gas turbine are obtained, wherein the flue gas carbon monoxide content monitoring model request carries a gas turbine identifier; then, a target joint energy station is determined based on the gas turbine identifier; thereafter, a joint flue gas carbon monoxide content monitoring model is constructed based on the operating data of the gas turbines corresponding to each of the target joint energy stations; finally, the flue gas carbon monoxide content of the target gas turbine is generated based on the joint flue gas carbon monoxide content monitoring model and the operating data of the target gas turbine. In summary, the technical solution of the present invention does not require the use of a sensor to measure the flue gas carbon monoxide content, and can monitor the flue gas carbon monoxide content in real time, facilitate maintenance, and reduce measurement errors. At the same time, the joint flue gas carbon monoxide content monitoring model relies on real data and is not easily affected by the external environment, and can more accurately determine the flue gas carbon monoxide content. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 A schematic flow chart of a method for monitoring carbon monoxide content in flue gas of a gas turbine provided in one embodiment of the present invention;

[0013] Figure 2 A schematic structural diagram of another method for monitoring carbon monoxide content in flue gas of a gas turbine provided by one embodiment of the present invention;

[0014] Figure 3 A schematic structural diagram of a device for monitoring carbon monoxide content in flue gas of a gas turbine provided by one embodiment of the present invention;

[0015] Figure 4 A schematic structural diagram of an electronic device provided by one embodiment of the present invention;

[0016] Figure 5 A scenario application diagram provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0018] In order to illustrate the technical solution of the present invention, specific embodiments are provided below.

[0019] like Figure 1 FIG. 1 shows a method for monitoring the carbon monoxide content in flue gas of a gas turbine according to an embodiment of the present invention. The method provided by the embodiment of the present invention can be applied to electronic devices, specifically servers or general computers. The embodiment of the present invention provides a method for monitoring the carbon monoxide content in flue gas of a gas turbine, comprising the following steps:

[0020] Step 101: Obtain a flue gas carbon monoxide content monitoring model request of a target gas turbine in a to-be-monitored energy station and operating data of the target gas turbine, wherein the flue gas carbon monoxide content monitoring model request carries a gas turbine identifier.

[0021] In some embodiments, the execution entity may obtain a flue gas carbon monoxide content monitoring model request and operating data of the target gas turbine in the energy station to be monitored by wired or wireless means, wherein the flue gas carbon monoxide content monitoring model request carries a gas turbine identification.

[0022] Specifically, the client containing the energy station to be monitored sends a request for the flue gas carbon monoxide content monitoring model for the energy station to be monitored via an API interface. The gas turbine identifier is used to identify the gas turbine within the energy station to be monitored. This identifier may include information such as the energy station to which the gas turbine belongs, the number of gas turbines, the gas turbine signal, operating environment, rated power, rated efficiency, and manufacturer. The specific gas turbine identifier should be determined based on actual needs. In this case, the gas turbines in the energy station to be monitored may be of the same model or similar.

[0023] In practical applications, a federated learning IoT platform can be developed. This platform is used to conduct joint learning with a federated learning client (the client performing the joint learning) to obtain and store the joint learning model. The client corresponding to the energy station to be monitored sends a request for a flue gas carbon monoxide content monitoring model through an API interface, and the federated learning IoT platform obtains the request.

[0024] Specifically, the energy station is generally configured to provide energy to a designated area, such as an area adjacent to the energy station. The energy system may include multiple energy stations, and supply energy to multiple areas through the multiple energy stations. The multiple gas turbines in the energy station are similar or may be of the same model. In an embodiment of the present application, each energy station serves as a node in an Internet of Things and is provided with a client. If the data of the energy station is used for joint learning, the client corresponding to the energy station is referred to as a joint learning client. Among them, joint learning ensures that user privacy data is protected to the greatest extent possible through distributed training and encryption technology, so as to enhance the user's trust in artificial intelligence technology. In an embodiment of the present application, under the joint learning mechanism, each participant (the joint learning client corresponding to the target joint energy station) contributes the encrypted data model to the alliance (joint learning Internet of Things platform) to jointly train a joint learning model.

[0025] Step 102: Determine a target combined energy station according to the gas turbine identifier.

[0026] In some embodiments, the execution entity may determine a target integrated energy station based on the gas turbine identifier. Based on information contained in the combustion engine identifier, several target integrated energy stations are determined, where the gas turbines in the target integrated energy stations are similar to the gas turbines in the energy station to be monitored, thereby ensuring the reference value of the target integrated energy station relative to the energy station to be monitored.

[0027] In actual applications, after the joint learning IoT platform obtains the flue gas carbon monoxide content monitoring model request of the energy station to be monitored, it determines the target joint energy station based on the gas turbine identification.

[0028] In some optional implementations of some embodiments, based on the gas turbine identification, the description information of the gas turbine in the energy station to be monitored and the description information of the candidate gas turbine in the candidate combined energy station are obtained; for each of the candidate combined energy stations, the similarity between the energy station to be monitored and the candidate combined energy station is determined based on the description information of the gas turbine in the energy station to be monitored and the description information of the candidate gas turbine in the candidate combined energy station; and the target combined energy station is determined based on the similarity between each of the candidate combined energy stations and the energy station to be monitored.

[0029] The method obtains descriptive information of the gas turbine within the energy station to be monitored, as carried by the gas turbine identifier. Then, descriptive information of candidate gas turbines within multiple candidate joint energy stations that can participate in joint learning is obtained. The similarity between the gas turbine descriptive information and the candidate gas turbine descriptive information is calculated to determine the similarity between the candidate joint energy station and the energy station to be monitored. Based on the similarity between the candidate joint energy station and the energy station to be monitored, a target joint energy station is determined. The descriptive information includes multiple parameters and parameter values ​​for each parameter. These multiple parameters include, but are not limited to, rated capacity, rated efficiency, operating mode, model (indicating the performance, specifications, and size of the gas turbine), brand, and operating location, and the specific parameters need to be determined based on actual circumstances. It is understood that the higher the similarity between the gas turbine descriptive information and the candidate gas turbine descriptive information, the higher the reference value of the candidate gas turbine, thereby ensuring the accuracy of the subsequent predicted target joint energy station. Preferably, the gas turbine and the candidate gas turbine should be of the same model.

[0030] As a possible scenario, when the similarity between the candidate combined energy station and the energy station to be monitored is not less than a preset threshold, the candidate combined energy station is determined as the target combined energy station. Specifically, the similarity between the candidate combined energy station and the energy station to be monitored can be determined by comparing the similarity between the description information of the gas turbine and the candidate gas turbine. In one example, based on the parameter value of each parameter in the description information of the gas turbine and the candidate gas turbine, the similarity of each parameter in the description information of the gas turbine and the candidate gas turbine is determined, the similarity of each parameter is weighted averaged, and the result is determined as the similarity between the candidate combined energy station and the energy station to be monitored. In actual applications, it is also possible to train a classification model with the description information of the gas turbine as the model input and the target combined energy station as the model output, and input the description information of the candidate gas turbine into the trained classification model to determine whether its corresponding candidate combined energy station is the target combined energy station.

[0031] In some optional implementations of some embodiments, a reference joint energy station is determined from each of the candidate joint energy stations based on the similarity between each of the candidate joint energy stations and the energy station to be monitored; a joint learning invitation is sent to each of the joint learning clients corresponding to the reference joint energy stations; and each of the reference joint energy stations that agrees to the joint learning invitation is determined as a target joint energy station.

[0032] Here, the candidate joint energy stations with a similarity not less than a preset threshold are determined as reference joint energy stations, and joint learning invitations are sent to the joint learning clients corresponding to each reference joint energy station. If the joint learning client feedbacks that the invitation is accepted, the corresponding reference joint energy station is determined as the target joint energy station.

[0033] Step 103: constructing a joint flue gas carbon monoxide content monitoring model based on the gas turbine operating data corresponding to each of the target joint energy stations.

[0034] In some embodiments, the execution entity may construct a joint flue gas carbon monoxide content monitoring model based on the gas turbine operation data corresponding to each of the target joint energy stations.

[0035] In actual applications, joint learning is carried out based on the gas turbine operating data in the joint learning client corresponding to each target joint energy station and the above-mentioned joint learning Internet of Things platform to build a joint flue gas carbon monoxide content monitoring model.

[0036] In some optional implementations of some embodiments, a model to be trained is obtained and sent to a joint learning client corresponding to each of the target joint energy stations; a local flue gas carbon monoxide content monitoring model of each of the joint learning clients is obtained, and the local flue gas carbon monoxide content monitoring model is obtained by joint learning based on the gas turbine operating data in the joint learning client and the model to be trained; and a joint flue gas carbon monoxide content monitoring model is constructed based on the local flue gas carbon monoxide content monitoring model of each of the joint learning clients.

[0037] In actual applications, during the joint learning process, it is necessary to determine the model to be trained and send the model to be trained to the joint learning client corresponding to each target joint energy station, so that the joint learning client can use the gas turbine operation data uploaded externally to train the model to be trained, obtain the model parameters and upload them to the joint learning Internet of Things platform, receive the aggregated model parameters issued by the joint learning Internet of Things platform to perform model iteration, and after the training is terminated, the joint learning client obtains the local flue gas carbon monoxide content monitoring model. The joint learning Internet of Things platform aggregates the model parameters of the local flue gas carbon monoxide content monitoring model finally uploaded by each joint learning client to construct a joint flue gas carbon monoxide content monitoring model.

[0038] Specifically, the model to be trained can be a model that has been developed or not developed in the prior art. Examples of existing models that can be used include, but are not limited to, back-propagation (BP) neural networks, support vector machines (SVMs), and XGBoost models (XGBoost is a boosted tree model that integrates many tree models to form a powerful classifier). The specific model needs to be determined based on actual circumstances. It should be understood that the model to be trained on each federated learning client is the same.

[0039] Step 104 : generating the flue gas carbon monoxide content of the target gas turbine according to the combined flue gas carbon monoxide content monitoring model and the operating data of the target gas turbine.

[0040] In some embodiments, the execution entity may generate the flue gas CO content of the target gas turbine based on the combined flue gas CO content monitoring model and the operating data of the target gas turbine. It should be understood that the resulting combined flue gas CO content monitoring model comprehensively considers the operating data of other gas turbines, thereby having relatively high accuracy and achieving model transfer.

[0041] In some optional implementations of some embodiments, the flue gas carbon monoxide content monitoring model request is sent by the client corresponding to the energy station to be monitored; predicting the flue gas carbon monoxide content of the gas turbine in the energy station to be monitored based on the joint flue gas carbon monoxide content monitoring model includes: calling the joint flue gas carbon monoxide content monitoring model in the model database, and sending the joint flue gas carbon monoxide content monitoring model to the client corresponding to the energy station to be monitored, so that the client downloads the joint flue gas carbon monoxide content monitoring model, and predicts the flue gas carbon monoxide content of the gas turbine through the downloaded joint flue gas carbon monoxide content monitoring model.

[0042] In actual application, the real-time operating data of the gas turbine is uploaded to the corresponding client, and the client substitutes the real-time data into the downloaded joint flue gas carbon monoxide content monitoring model to predict the flue gas carbon monoxide content of the gas turbine.

[0043] In some optional implementations of some embodiments, the combined flue gas carbon monoxide content monitoring model is added to a model database; when the flue gas carbon monoxide content monitoring model request is received again, the combined flue gas carbon monoxide content monitoring model in the model database is called, and the combined flue gas carbon monoxide content monitoring model is sent to the client corresponding to the energy station to be monitored.

[0044] In actual applications, the joint flue gas carbon monoxide content monitoring model is stored in the model database on the joint learning Internet of Things platform. If the joint flue gas carbon monoxide content monitoring model needs to be called again in the future, the joint flue gas carbon monoxide content monitoring model is directly sent to the client corresponding to the energy station to be monitored without the need for model training, so as to quickly obtain the model.

[0045] It should be understood that the energy station to be monitored may have multiple gas turbines, and the same combined flue gas CO content monitoring model may be used to predict flue gas CO content for these multiple gas turbines. The differences between the gas turbines within a monitored energy station are generally small. Therefore, directly using the same combined flue gas CO content monitoring model to predict flue gas CO content for different gas turbines can reduce computational complexity and improve efficiency while ensuring prediction accuracy.

[0046] In actual applications, the predicted carbon monoxide content of the flue gas of the gas turbine in the energy station to be monitored is uploaded to the joint learning Internet of Things platform.

[0047] In some optional implementations of some embodiments, the carbon monoxide content of the flue gas of the target gas turbine is sent to a terminal device with a display function and displayed.

[0048] Some embodiments of the present disclosure disclose a method for monitoring the flue gas carbon monoxide content of a gas turbine. First, a flue gas carbon monoxide content monitoring model request of a target gas turbine in an energy station to be monitored and operating data of the target gas turbine are obtained, wherein the flue gas carbon monoxide content monitoring model request carries a gas turbine identifier; then, a target joint energy station is determined based on the gas turbine identifier; thereafter, a joint flue gas carbon monoxide content monitoring model is constructed based on the operating data of the gas turbines corresponding to each of the target joint energy stations; finally, the flue gas carbon monoxide content of the target gas turbine is generated based on the joint flue gas carbon monoxide content monitoring model and the operating data of the target gas turbine. In summary, through the technical solution of the present invention, there is no need to use a sensor to measure the flue gas carbon monoxide content, and the flue gas carbon monoxide content can be monitored in real time, which is convenient for maintenance and reduces measurement errors. At the same time, the joint flue gas carbon monoxide content monitoring model relies on real data and is not easily affected by the external environment, so the flue gas carbon monoxide content can be determined more accurately.

[0049] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0050] Figure 1 What is shown is only a basic embodiment of the method of the present invention. By performing certain optimization and expansion on this basis, other preferred embodiments of the method can be obtained.

[0051] like Figure 2FIGURE 1 illustrates another specific embodiment of the method for monitoring carbon monoxide content in flue gas from a gas turbine according to the present invention. This embodiment, based on the previous embodiments, provides a more detailed description in conjunction with an application scenario. It should be understood that the method described in this embodiment is equally applicable in other relevant scenarios.

[0052] The specific scenarios combined with this embodiment are: Figure 5 As shown in the figure, it is assumed that there are three energy stations (several gas turbines in the energy stations are similar) and a joint learning IoT platform T. The three energy stations are joint energy station A, joint energy station B and energy station C to be monitored. The joint learning IoT platform T interacts with joint learning clients Ac, joint learning clients Bc and monitoring clients Cc. Among them, joint energy stations A and B have rich data and can use their own gas turbine operation data to train the local flue gas carbon monoxide content monitoring model, which is the contributor of the joint learning model; the gas turbine in the energy station C to be monitored has no flue gas. The joint learning client Ac stores the operating data of gas turbines A1, A2, and A3 in the joint energy station A; the joint learning client Bc stores the operating data of gas turbines B1, B2, and B3 in the joint energy station B; and the monitoring client Cs stores the operating data of gas turbines C1, C2, and C3 in the monitored energy station C. The joint learning client Ac, the joint learning client Bc, and the monitoring client Cc are respectively deployed on local operation and maintenance servers As, Bs, and Cs. Here, the local operation and maintenance server refers to the server responsible for gas turbine operation and maintenance. The method described in this embodiment aims to construct a joint flue gas carbon monoxide content monitoring model based on the gas turbine operating data in the joint learning client to predict the flue gas carbon monoxide content of the gas turbines in the monitored energy station.

[0053] The method described in this embodiment includes the following steps:

[0054] Step 201: Obtain a flue gas carbon monoxide content monitoring model request of a target gas turbine in a to-be-monitored energy station and operating data of the target gas turbine, wherein the flue gas carbon monoxide content monitoring model request carries a gas turbine identifier.

[0055] The energy station C to be monitored initiates a request for a flue gas carbon monoxide content measurement model for the gas turbine, i.e., a flue gas carbon monoxide content monitoring model request, to the federated learning IoT platform T through the monitoring client Cc and API interface deployed on the local operation and maintenance server Cs.

[0056] Step 202: According to the gas turbine identifier, obtain description information of the gas turbine in the energy station to be monitored and description information of the candidate gas turbine in the candidate combined energy station.

[0057] The joint learning IoT platform T parses the gas turbine identifier to obtain descriptive information of the gas turbine in the monitored energy station C. The descriptive information is the model of gas turbine C1. Here, the models of gas turbines C1, C2, and C3 are the same. The model of gas turbine A1 in the joint energy station A is obtained. The models of gas turbines A1, A2, and A3 are the same. The model of gas turbine B1 in the joint energy station B is obtained. The models of gas turbines B1, B2, and B3 are the same.

[0058] Step 203: For each candidate integrated energy station, determine the similarity between the energy station to be monitored and the candidate integrated energy station based on the description information of the gas turbine in the energy station to be monitored and the description information of the candidate gas turbine in the candidate integrated energy station.

[0059] The model of gas turbine A1 in combined energy station A is the same as that of gas turbine C1, and the similarity between energy station C to be monitored and combined energy station A is 1. The model of gas turbine B1 in combined energy station B is the same as that of gas turbine C1, and the similarity between energy station C to be monitored and combined energy station B is 1.

[0060] Step 204 : Determine a reference integrated energy station from among the candidate integrated energy stations based on the similarity between each candidate integrated energy station and the energy station to be monitored.

[0061] The combined energy station A and the combined energy station B are respectively determined as reference combined energy stations.

[0062] Step 205: Send a joint learning invitation to each joint learning client corresponding to each of the reference joint energy stations; and determine each of the reference joint energy stations that agrees to the joint learning invitation as a target joint energy station.

[0063] The joint learning IoT platform T sends a joint learning invitation to the joint learning client Ac and the joint learning client Bc. If the joint learning client Ac and the joint learning client Bc return an agreement, the joint energy station A and the joint energy station B are respectively determined as the target joint energy stations.

[0064] Step 206: Obtain the local flue gas carbon monoxide content monitoring model of each of the joint learning clients, and construct a joint flue gas carbon monoxide content monitoring model based on the local flue gas carbon monoxide content monitoring model of each of the joint learning clients.

[0065] The joint learning client Ac trains the to-be-trained model based on the operating data of gas turbines A1, A2, and A3, and uploads the model parameters to the joint learning IoT platform T through the API interface. The joint learning client Bc trains the to-be-trained model based on the operating data of gas turbines B1, B2, and B3, and uploads the model parameters to the joint learning IoT platform T. The joint learning IoT platform T sends the aggregated model parameters to the joint learning clients Ac and Bc for model iteration. The joint learning clients Ac and Bc each obtain a local flue gas carbon monoxide content monitoring model. The joint learning IoT platform T integrates the local flue gas carbon monoxide content monitoring models obtained by the joint learning clients Ac and Bc to obtain a joint flue gas carbon monoxide content monitoring model.

[0066] Step 207: Call the combined flue gas carbon monoxide content monitoring model in the model database, and send the combined flue gas carbon monoxide content monitoring model to the client corresponding to the energy station to be monitored, so that the client can download the combined flue gas carbon monoxide content monitoring model and predict the flue gas carbon monoxide content of the gas turbine through the downloaded combined flue gas carbon monoxide content monitoring model.

[0067] The joint learning IoT platform T sends the joint flue gas carbon monoxide content monitoring model to the monitoring client Cc. The monitoring client Cc downloads the joint flue gas carbon monoxide content monitoring model and uses the model to predict the flue gas carbon monoxide content of gas turbines C1, C2, and C3.

[0068] Through the above technical solution, it can be seen that the beneficial effect of this embodiment is: based on the interaction between the joint learning Internet of Things platform and the gas turbine client, model calling is realized, thereby quickly and accurately predicting the oxygen content of the gas turbine flue gas in real time.

[0069] Figure 3Schematic diagram of a gas turbine flue gas carbon monoxide content monitoring device 300 provided in an embodiment of the present invention; the gas turbine flue gas carbon monoxide content monitoring device 300 comprises: an acquisition module 301, a determination module 302, a construction module 303, and a generation module 304. The acquisition module 301 is configured to acquire a flue gas carbon monoxide content monitoring model request and operating data of a target gas turbine in a monitored energy station, wherein the flue gas carbon monoxide content monitoring model request carries a gas turbine identifier; the determination module 302 is configured to determine a target joint energy station based on the gas turbine identifier; the construction module 303 is configured to construct a joint flue gas carbon monoxide content monitoring model based on the operating data of the gas turbines corresponding to each of the target joint energy stations; and the generation module 304 is configured to generate the flue gas carbon monoxide content of the target gas turbine based on the joint flue gas carbon monoxide content monitoring model and the operating data of the target gas turbine.

[0070] In some optional implementations of some embodiments, the determination module 302 in the above-mentioned gas turbine flue gas carbon monoxide content monitoring device 300 includes: an acquisition unit, configured to acquire the description information of the gas turbine in the energy station to be monitored and the description information of the candidate gas turbine in the candidate combined energy station according to the gas turbine identification; a first determination unit, configured to determine, for each of the candidate combined energy stations, the similarity between the energy station to be monitored and the candidate combined energy station according to the description information of the gas turbine in the energy station to be monitored and the description information of the candidate gas turbine in the candidate combined energy station; and a second determination unit, configured to determine the target combined energy station according to the similarity between each of the candidate combined energy stations and the energy station to be monitored.

[0071] In some optional implementations of some embodiments, the second determination unit in the above-mentioned gas turbine flue gas carbon monoxide content monitoring device 300 is further configured to: determine a reference joint energy station from each of the candidate joint energy stations based on the similarity between each of the candidate joint energy stations and the energy station to be monitored; send a joint learning invitation to each of the joint learning clients corresponding to each of the reference joint energy stations; and determine each of the reference joint energy stations that agrees to the joint learning invitation as a target joint energy station.

[0072] In some optional implementations of some embodiments, the construction module 303 in the above-mentioned gas turbine flue gas carbon monoxide content monitoring device 300 is further configured to: obtain the model to be trained, and send the model to be trained to the joint learning client corresponding to each of the target joint energy stations; obtain the local flue gas carbon monoxide content monitoring model of each of the joint learning clients, and the local flue gas carbon monoxide content monitoring model is obtained by joint learning based on the gas turbine operation data in the joint learning client and the model to be trained; and construct a joint flue gas carbon monoxide content monitoring model based on the local flue gas carbon monoxide content monitoring model of each of the joint learning clients.

[0073] In some optional implementations of some embodiments, the flue gas carbon monoxide content monitoring model request is sent by the client corresponding to the energy station to be monitored; the prediction of the flue gas carbon monoxide content of the gas turbine in the energy station to be monitored based on the joint flue gas carbon monoxide content monitoring model is further configured to: call the joint flue gas carbon monoxide content monitoring model in the model database, and send the joint flue gas carbon monoxide content monitoring model to the client corresponding to the energy station to be monitored, so that the client downloads the joint flue gas carbon monoxide content monitoring model, and predicts the flue gas carbon monoxide content of the gas turbine through the downloaded joint flue gas carbon monoxide content monitoring model.

[0074] In some optional implementations of some embodiments, the flue gas carbon monoxide content monitoring device 300 of the above-mentioned gas turbine is further configured to: add the combined flue gas carbon monoxide content monitoring model to the model database; when the flue gas carbon monoxide content monitoring model request is received again, call the combined flue gas carbon monoxide content monitoring model in the model database, and send the combined flue gas carbon monoxide content monitoring model to the client corresponding to the energy station to be monitored.

[0075] In some optional implementations of some embodiments, the gas turbine flue gas carbon monoxide content monitoring device 300 is further configured to: send the flue gas carbon monoxide content of the target gas turbine to a terminal device with a display function and display it.

[0076] It is understood that the units described in the device 300 are similar to those in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the device 300 and the units included therein, and will not be repeated here.

[0077] Figure 4FIG. 1 is a schematic diagram of a method and apparatus for monitoring carbon monoxide content in flue gas of a gas turbine according to an embodiment of the present invention. Figure 4 As shown, the method and apparatus 4 for monitoring the carbon monoxide content in flue gas of a gas turbine of this embodiment includes: a processor 40, a memory 41, and a computer program 42 stored in the memory 41 and executable on the processor 40. When the processor 40 executes the computer program 42, the steps in the above-mentioned embodiments of the method for testing the thermal efficiency of a gas-fired industrial boiler are implemented, for example Figure 1 Alternatively, when the processor 40 executes the computer program 42, the functions of the modules / units in the above-mentioned device embodiments are realized, for example, Figure 4 Functions of modules 401 to 404 are shown.

[0078] Exemplarily, the computer program 42 can be divided into one or more modules / units, which are stored in the memory 41 and executed by the processor 40 to implement the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 42 in the gas turbine flue gas carbon monoxide content monitoring method and device 4. For example, the computer program 42 can be divided into a synchronization module, a summary module, an acquisition module, and a return module (a module in a virtual device). The specific functions of each module are as follows:

[0079] The method and device 4 for monitoring the carbon monoxide content in flue gas of a gas turbine can be a computing device such as a desktop computer, a notebook computer, a palmtop computer, or a cloud server. The method and device for monitoring the carbon monoxide content in flue gas of a gas turbine can include, but are not limited to, a processor 40 and a memory 41. It can be understood by those skilled in the art that Figure 4 The present invention is merely an example of a method and apparatus 4 for monitoring the carbon monoxide content in flue gas of a gas turbine, and does not constitute a limitation on the method and apparatus 4 for monitoring the carbon monoxide content in flue gas of a gas turbine. The method and apparatus 4 may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, the method and apparatus for monitoring the carbon monoxide content in flue gas of a gas turbine may also include input and output devices, network access devices, buses, etc.

[0080] The processor 40 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0081] The memory 41 may be an internal storage unit of the method and apparatus for monitoring carbon monoxide content in flue gas of a gas turbine 4, such as a hard disk or memory of the method and apparatus for monitoring carbon monoxide content in flue gas of a gas turbine 4. The memory 41 may also be an external storage device of the method and apparatus for monitoring carbon monoxide content in flue gas of a gas turbine 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the method and apparatus for monitoring carbon monoxide content in flue gas of a gas turbine 4. Furthermore, the memory 41 may include both an internal storage unit and an external storage device of the method and apparatus for monitoring carbon monoxide content in flue gas of a gas turbine 4. The memory 41 is used to store the computer program and other programs and data required by the method and apparatus for monitoring carbon monoxide content in flue gas of a gas turbine. The memory 41 may also be used to temporarily store data that has been output or is about to be output.

[0082] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment 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. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0083] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0084] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0085] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0086] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0087] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0088] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0089] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for monitoring carbon monoxide content in flue gas of a gas turbine, characterized in that: include: Obtaining a flue gas carbon monoxide content monitoring model request and operating data of a target gas turbine in a to-be-monitored energy station, wherein the flue gas carbon monoxide content monitoring model request carries a gas turbine identifier; determining a target combined energy station according to the gas turbine identifier; Constructing a joint flue gas carbon monoxide content monitoring model based on the gas turbine operating data corresponding to each of the target joint energy stations; generating the flue gas carbon monoxide content of the target gas turbine according to the combined flue gas carbon monoxide content monitoring model and the operating data of the target gas turbine; The step of determining a target combined energy station according to the gas turbine identifier includes: According to the gas turbine identifier, obtaining description information of the gas turbine in the energy station to be monitored and description information of the candidate gas turbine in the candidate combined energy station; For each candidate integrated energy station, determining a similarity between the energy station to be monitored and the candidate integrated energy station based on description information of the gas turbine in the energy station to be monitored and description information of the candidate gas turbine in the candidate integrated energy station; Determining a target unified energy station based on the similarities between each of the candidate unified energy stations and the energy station to be monitored; The method of constructing a joint flue gas carbon monoxide content monitoring model based on the gas turbine operating data corresponding to each target joint energy station includes: Obtaining a model to be trained, and sending the model to be trained to a joint learning client corresponding to each of the target joint energy stations; Obtaining a local flue gas carbon monoxide content monitoring model for each of the joint learning clients, wherein the local flue gas carbon monoxide content monitoring model is obtained by joint learning based on the gas turbine operating data in the joint learning client and the model to be trained; A joint flue gas carbon monoxide content monitoring model is constructed according to the local flue gas carbon monoxide content monitoring models of each of the joint learning clients.

2. The method for monitoring carbon monoxide content in flue gas of a gas turbine according to claim 1, wherein: The step of determining a target unified energy station based on the similarities between each of the candidate unified energy stations and the energy station to be monitored includes: determining a reference integrated energy station from among the candidate integrated energy stations according to the similarity between each of the candidate integrated energy stations and the energy station to be monitored; Sending a joint learning invitation to each joint learning client corresponding to each of the reference joint energy stations; Each reference joint energy station that agrees to the joint learning invitation is determined as a target joint energy station.

3. The method for monitoring carbon monoxide content in flue gas of a gas turbine according to claim 1, wherein: The flue gas carbon monoxide content monitoring model request is sent by the client corresponding to the energy station to be monitored; The step of predicting the flue gas carbon monoxide content of the gas turbine in the energy station to be monitored according to the combined flue gas carbon monoxide content monitoring model includes: The combined flue gas carbon monoxide content monitoring model in the model database is called, and the combined flue gas carbon monoxide content monitoring model is sent to the client corresponding to the energy station to be monitored, so that the client downloads the combined flue gas carbon monoxide content monitoring model and predicts the flue gas carbon monoxide content of the gas turbine through the downloaded combined flue gas carbon monoxide content monitoring model.

4. The method for monitoring carbon monoxide content in flue gas of a gas turbine according to claim 1, wherein: The method further comprises: Adding the combined flue gas carbon monoxide content monitoring model to a model database; When the flue gas carbon monoxide content monitoring model request is received again, the combined flue gas carbon monoxide content monitoring model in the model database is called, and the combined flue gas carbon monoxide content monitoring model is sent to the client corresponding to the energy station to be monitored.

5. The method for monitoring carbon monoxide content in flue gas of a gas turbine according to claim 1, wherein: The method further comprises: The carbon monoxide content of the flue gas of the target gas turbine is sent to a terminal device with a display function and displayed.

6. A gas-fired industrial boiler thermal efficiency testing device, characterized in that: include: an acquisition module configured to acquire a flue gas carbon monoxide content monitoring model request of a target gas turbine in a to-be-monitored energy station and operating data of the target gas turbine, wherein the flue gas carbon monoxide content monitoring model request carries a gas turbine identifier; a determination module configured to determine a target combined energy station according to the gas turbine identifier; a construction module configured to construct a joint flue gas carbon monoxide content monitoring model based on the gas turbine operation data corresponding to each of the target joint energy stations; a generating module configured to generate the flue gas carbon monoxide content of the target gas turbine according to the combined flue gas carbon monoxide content monitoring model and the operating data of the target gas turbine; The determination module is configured to obtain, based on the gas turbine identifier, description information of the gas turbine in the energy station to be monitored and description information of the candidate gas turbines in the candidate combined energy stations; for each candidate combined energy station, determine, based on the description information of the gas turbine in the energy station to be monitored and the description information of the candidate gas turbines in the candidate combined energy stations, a similarity between the energy station to be monitored and the candidate combined energy stations; and determine a target combined energy station based on the similarity between each candidate combined energy station and the energy station to be monitored. The construction module is used to obtain the model to be trained and send the model to be trained to the joint learning client corresponding to each of the target joint energy stations; obtain the local flue gas carbon monoxide content monitoring model of each of the joint learning clients, and the local flue gas carbon monoxide content monitoring model is obtained by joint learning based on the gas turbine operation data in the joint learning client and the model to be trained; and construct a joint flue gas carbon monoxide content monitoring model based on the local flue gas carbon monoxide content monitoring model of each of the joint learning clients.

7. A terminal device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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