CN114819237A - Method and device for predicting oxygen content of flue gas of gas-fired boiler - Google Patents
Method and device for predicting oxygen content of flue gas of gas-fired boiler Download PDFInfo
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- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 title claims abstract description 211
- 239000001301 oxygen Substances 0.000 title claims abstract description 211
- 229910052760 oxygen Inorganic materials 0.000 title claims abstract description 211
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- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 title claims abstract description 204
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- MCMNRKCIXSYSNV-UHFFFAOYSA-N Zirconium dioxide Chemical compound O=[Zr]=O MCMNRKCIXSYSNV-UHFFFAOYSA-N 0.000 description 2
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Abstract
The invention is suitable for the field of gas distributed energy, and provides a method and a device for predicting the oxygen content of flue gas of a gas boiler, wherein the method comprises the following steps: acquiring a flue gas oxygen content prediction model request of a target gas-fired boiler in an energy station to be monitored and operation data of the target gas-fired boiler, wherein the flue gas oxygen content prediction model request carries a gas-fired boiler identifier; determining a target combined energy station according to the gas boiler identifier; establishing a combined flue gas oxygen content prediction model according to the gas-fired boiler operation data corresponding to each target combined energy station; and generating the oxygen content of the flue gas of the target gas-fired boiler according to the combined flue gas oxygen content prediction model and the operation data of the target gas-fired boiler. This embodiment has realized need not to use the sensor to measure the oxygen content of discharging fume, can real-time supervision oxygen content of discharging fume, and the maintenance of being convenient for reduces measuring error, and the joint oxygen content of discharging fume measurement model relies on true data simultaneously, is difficult to receive external environment's influence, can comparatively accurate definite oxygen content of discharging fume.
Description
Technical Field
The invention belongs to the field of gas distributed energy, and particularly relates to a method and a device for predicting the oxygen content of flue gas of a gas boiler.
Background
The oxygen content of the flue gas refers to the content of the flue gas discharged after the fuel is combusted, and the oxygen content is an important index of the combustion of the gas-fired boiler, and the value of the oxygen content is related to factors such as the structure of the gas-fired boiler, the type and the property of the fuel, the size of a load, the operating air distribution condition, the sealing condition of equipment and the like. In order to allow sufficient combustion of the fuel during actual operation of the gas boiler, the amount of air actually supplied is much larger than the theoretical amount of air supplied. This is the amount of air supplied, which we generally call excess air. The excess air ratio is the ratio of the actual supplied air amount to the theoretical supplied air amount. The oxygen content of the flue gas is too low, namely the smaller the excess air coefficient is, the insufficient oxygen supplied to the gas-fired boiler for combustion can be caused, the fuel can not be fully combusted, and the heat loss is increased; the oxygen content of the flue gas is too high, namely the larger the excess air coefficient is, the heat efficiency of the gas-fired boiler is reduced, the combustion is influenced, the emission of environmental pollutants is easy to exceed the standard, and the power consumption of the flue gas is increased. Therefore, the oxygen content of the flue gas of the gas-fired boiler is controlled within a reasonable range, and the oxygen content of the flue gas of the gas-fired boiler has important significance for saving energy, maintaining economic combustion of the gas-fired boiler and realizing safe, efficient and low-pollution emission, in other words, the oxygen content of the flue gas of the gas-fired boiler is one of important signs for measuring whether the gas-fired boiler operates safely, economically and environmentally.
Currently, zirconia oxide sensors are commonly used in the industry to measure the oxygen content of flue gases.
However, the sensor has many defects of high cost, large measurement lag, difficult maintenance, large measurement error, short service life and the like, and is not suitable for long-term use in the scene that distributed energy sources mainly comprise small and medium-sized gas boilers.
Disclosure of Invention
In view of this, the embodiment of the present invention provides a method and an apparatus for predicting an oxygen content in flue gas of a gas boiler, which only need a small amount of tag data, can overcome a disadvantage of a complete actual measurement of a sensor, and do not need to rely on a large number of physical parameters like physical modeling.
The first aspect of the embodiment of the invention provides a method for predicting the oxygen content of flue gas of a gas-fired boiler, which comprises the following steps: acquiring a flue gas oxygen content prediction model request of a target gas-fired boiler in an energy station to be monitored and operation data of the target gas-fired boiler, wherein the flue gas oxygen content prediction model request carries a gas-fired boiler identifier; determining a target combined energy station according to the gas boiler identifier; establishing a combined flue gas oxygen content prediction model according to the gas-fired boiler operation data corresponding to each target combined energy station; and generating the oxygen content of the flue gas of the target gas-fired boiler according to the combined flue gas oxygen content prediction model and the operation data of the target gas-fired boiler.
A second aspect of the embodiments of the present invention provides a device for predicting an oxygen content in flue gas of a gas boiler, including: the system comprises an acquisition module, a monitoring module and a monitoring module, wherein the acquisition module is configured to acquire a flue gas oxygen content prediction model request of a target gas boiler in an energy station to be monitored and operation data of the target gas boiler, and the flue gas oxygen content prediction model request carries a gas boiler identifier; a determination module configured to determine a target combined energy station based on the gas boiler identification; the construction module is configured to construct a joint flue gas oxygen content prediction model according to the gas-fired boiler operation data corresponding to each target joint energy station; a generating module configured to generate the flue gas oxygen content of the target gas boiler according to the combined flue gas oxygen content prediction model and the operational data of the target gas boiler.
A third aspect of embodiments 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.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium having a computer program stored thereon, where the computer program is adapted to perform the steps of the method according to any of the claims 1 to 7 when executed by a processor.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: firstly, acquiring a flue gas oxygen content prediction model request of a target gas-fired boiler in an energy station to be monitored and operation data of the target gas-fired boiler, wherein the flue gas oxygen content prediction model request carries a gas-fired boiler identifier; then, determining a target combined energy station according to the gas boiler identifier; then, establishing a combined flue gas oxygen content prediction model according to the gas boiler operation data corresponding to each target combined energy station; and finally, generating the oxygen content of the flue gas of the target gas-fired boiler according to the combined flue gas oxygen content prediction model and the operation data of the target gas-fired boiler. In conclusion, according to the technical scheme of the invention, the oxygen content of the flue gas can be monitored in real time without using a sensor to measure the oxygen content of the flue gas, the maintenance is convenient, the measurement error is reduced, meanwhile, the combined flue gas oxygen content prediction model depends on real data, the influence of the external environment is not easy to influence, and the oxygen content of the flue gas can be more accurately determined.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for predicting oxygen content in flue gas of a gas boiler according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of another method for predicting oxygen content in flue gas of a gas boiler according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a device for predicting oxygen content in flue gas of a gas boiler according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;
fig. 5 is a diagram of a scene application according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, 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.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
As shown in fig. 1, a method for predicting oxygen content in flue gas of a gas-fired boiler according to an embodiment of the present invention is provided. The method provided by the embodiment of the invention can be applied to electronic equipment, and particularly can be applied to a server or a general computer. The embodiment of the invention provides a method for predicting the oxygen content of flue gas of a gas-fired boiler, which comprises the following steps:
In some embodiments, the execution subject may obtain, in a wired or wireless manner, a flue gas oxygen content prediction model request of a target gas boiler in the energy station to be monitored, the flue gas oxygen content prediction model request carrying a gas boiler identifier, and operation data of the target gas boiler.
Specifically, a client side including the energy station to be monitored sends a flue gas oxygen content prediction model request of the energy station to be monitored through an API (application programming interface). The gas boiler identification is used for identifying the gas boilers in the energy stations to be monitored, and can comprise description information of the gas boilers belonging to the energy stations to be monitored, the number of the gas boilers, signals of the gas boilers, working environment, rated power, rated efficiency, manufacturers and the like, and the gas boiler identification is determined according to actual requirements. Here, several gas boilers in the energy station to be monitored are of the same model or several gas boilers are similar.
In practical application, a joint learning internet of things platform can be developed, and the joint learning internet of things platform is used for performing joint learning with a joint learning client (a client performing joint learning), so that a joint learning model is obtained, and the joint learning model is stored. And the client corresponding to the energy station to be monitored sends a flue gas oxygen content prediction model request through the API, and the joint learning Internet of things platform acquires the flue gas oxygen content prediction model request.
In particular, the energy station is generally configured to provide energy to a designated area, such as an area adjacent to the energy station, and the energy system may include a plurality of energy stations and provide energy to a plurality of areas through the plurality of energy stations, and the plurality of gas boilers in the energy stations may be similar or may be the same type. In the embodiment of the application, each energy station is used as a node in the internet of things and is provided with a client, and if the data of the energy station is used for joint learning, the client corresponding to the energy station is called as a joint learning client. The joint learning ensures that the private data of the user is protected to the maximum extent through a distributed training and encryption technology so as to improve the trust of the user on the artificial intelligence technology. In the embodiment of the application, under a joint learning mechanism, each participant (a joint learning client corresponding to each target joint energy station) contributes the encrypted data model to a alliance (a joint learning internet of things platform), and jointly trains a joint learning model.
And step 102, determining a target combined energy station according to the gas boiler identifier.
In some embodiments, the execution subject may determine the target combined energy station according to the gas boiler identification. And determining a plurality of target combined energy stations based on the information contained in the internal combustion engine identification of the gas turbine, wherein the gas boilers in the target combined energy stations are similar to the gas boilers in the energy stations to be monitored, and the reference value of the target combined energy stations relative to the energy stations to be monitored is ensured.
In practical application, after the joint learning Internet of things platform obtains a flue gas oxygen content prediction model request of an energy station to be monitored, a target joint energy station is determined according to a gas boiler identifier.
In some optional implementation manners of some embodiments, according to the gas boiler identifier, obtaining description information of the gas boiler in the energy station to be monitored and description information of a candidate gas boiler in a candidate combined energy station; for each candidate combined energy station, determining the similarity between the energy station to be monitored and the candidate combined energy station according to the description information of the gas boilers in the energy station to be monitored and the description information of the candidate gas boilers in the candidate combined energy station; and determining a target combined energy station according to the similarity between each candidate combined energy station and the energy station to be monitored.
The method comprises the steps of obtaining description information of the gas boilers in the energy station to be monitored, carried by gas boiler identification, of the candidate gas boilers in the multiple candidate combined energy stations capable of participating in combined learning, calculating similarity between the description information of the gas boilers and the description information of the candidate gas boilers to determine the similarity between the candidate combined energy stations and the energy station to be monitored, and determining a target combined energy station according to the similarity between the candidate combined energy stations and the energy station to be monitored. Wherein the description information comprises a plurality of parameters and a parameter value of each parameter. The parameters include, but are not limited to, rated capacity, rated efficiency, operation mode, model (indicating the performance, specification and size of the gas boiler), brand and operation place, and the specific needs are determined by combining actual conditions. It can be understood that the higher the similarity between the description information of the gas boiler and the description information of the candidate gas boiler, the higher the reference value of the candidate gas boiler, so as to ensure the accuracy of the target combined energy station predicted subsequently. Preferably, the gas boiler and the candidate gas boiler should be of the same model.
As a possible case, 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. The similarity between the candidate combined energy source station and the energy source station to be monitored can be determined by comparing the similarity of the description information between the gas boiler and the candidate gas boiler. In one example, the similarity of each parameter in the description information of the gas boiler and the candidate gas boiler is determined based on the parameter value of each parameter in the description information of the gas boiler and the candidate gas boiler, the similarity of each parameter is weighted and averaged, and the result is determined as the similarity between the candidate combined energy station and the energy station to be monitored. In practical application, the description information of the gas boiler can be used as model input, the target combined energy station is used as model output, a classification model is trained, and the description information of the candidate gas boiler is input into the trained classification model, so that whether the candidate combined energy station corresponding to the candidate gas boiler is the target combined energy station or not is determined.
In some optional implementations of some embodiments, a reference joint energy station is determined from each of the candidate joint energy stations according to a similarity between each of the candidate joint energy stations and the energy station to be monitored; respectively sending joint learning invitation to joint learning clients corresponding to the reference joint energy stations; and respectively determining all the reference joint energy stations agreeing with the joint learning invitation as target joint energy stations.
And if the joint learning client feeds back the invitation to pass, the corresponding reference joint energy station is determined as the target joint energy station.
103, constructing a combined flue gas oxygen content prediction model according to the gas boiler operation data corresponding to each target combined energy station.
In some embodiments, the execution body may construct a joint flue gas oxygen content prediction model according to gas boiler operation data corresponding to each target joint energy station.
In practical application, joint learning is carried out according to the gas boiler operation data in the joint learning client side corresponding to each target joint energy station and the joint learning Internet of things platform, and a joint flue gas oxygen content prediction model is constructed.
In some optional implementation manners of some embodiments, a model to be trained is obtained, and the model to be trained is sent to a joint learning client corresponding to each target joint energy station; acquiring a local flue gas oxygen content prediction model of each joint learning client, wherein the local flue gas oxygen content prediction model is obtained by joint learning based on gas boiler operation data in the joint learning client and the model to be trained; and constructing a combined flue gas oxygen content prediction model according to the respective local flue gas oxygen content prediction model of each combined learning client.
In practical application, in the process of performing joint learning, a model to be trained needs to be determined, the model to be trained is sent to joint learning client sides corresponding to target joint energy stations, the joint learning client sides can perform model training on the model to be trained by using externally uploaded gas boiler operation data, model parameters are obtained and uploaded to a joint learning internet of things platform, aggregated model parameters sent by the joint learning internet of things platform are received for model iteration, after training is ended, the joint learning client sides obtain local flue gas oxygen content prediction models, and the joint learning internet of things platform aggregates the model parameters of the local flue gas oxygen content prediction models uploaded by the joint learning client sides finally to construct the joint flue gas oxygen content prediction models.
Specifically, the model to be trained may be a developed model or an undeveloped model in the prior art, examples of available existing models include, but are not limited to, a Back Propagation (BP) neural network, a Support Vector Machine (SVM), and an XGBoost model (XGBoost is a lifting tree model, where a plurality of tree models are integrated together to form a strong classifier), and the like, and the specific requirements need to be determined in combination with actual situations. It should be understood that the models to be trained of the respective joint learning clients are the same.
And 104, generating the oxygen content of the flue gas of the target gas-fired boiler according to the combined flue gas oxygen content prediction model and the operation data of the target gas-fired boiler.
In some embodiments, the execution subject may generate the flue gas oxygen content of the target gas-fired boiler according to the combined flue gas oxygen content prediction model and the operation data of the target gas-fired boiler. It should be understood that the obtained combined flue gas oxygen content prediction model comprehensively considers the operation data of other gas-fired boilers, so that the method has relatively high accuracy and realizes model migration.
In some optional implementations of some embodiments, the flue gas oxygen content prediction model request is sent by a client corresponding to the energy station to be monitored; the predicting the oxygen content of the flue gas of the gas-fired boiler in the energy station to be monitored according to the combined flue gas oxygen content prediction model comprises the following steps: and calling the combined flue gas oxygen content prediction model in the model database, and sending the combined flue gas oxygen content prediction model to a client corresponding to the energy station to be monitored so that the client can download the combined flue gas oxygen content prediction model, and predicting the flue gas oxygen content of the gas boiler through the downloaded combined flue gas oxygen content prediction model.
In practical application, real-time operation data of the gas boiler are uploaded to the corresponding client, and the client substitutes the real-time data into the downloaded combined flue gas oxygen content prediction model to predict the flue gas oxygen content of the gas boiler.
In some optional implementations of some embodiments, the combined flue gas oxygen content prediction model is added to a model database; and when the request of the flue gas oxygen content prediction model is received again, calling the combined flue gas oxygen content prediction model in the model database, and sending the combined flue gas oxygen content prediction model to the client corresponding to the energy station to be monitored.
In practical application, the combined flue gas oxygen content prediction model is stored in a model database on the combined learning Internet of things platform, and if the combined flue gas oxygen content prediction model needs to be called again subsequently, the combined flue gas oxygen content prediction model is directly sent to a client corresponding to the energy station to be monitored, model training is not needed, and the model is rapidly obtained.
It should be understood that there may be multiple gas boilers in the energy station to be monitored, and multiple gas boilers use the same combined flue gas oxygen content prediction model for flue gas oxygen content prediction. For the energy station to be monitored, the difference of the gas boilers in the energy station to be monitored is generally small, so that the same combined flue gas oxygen content prediction model is directly adopted to predict the flue gas oxygen contents of different gas boilers, the calculated amount can be reduced on the premise of ensuring the prediction accuracy, and the calculation efficiency is improved.
In practical application, the predicted oxygen content of the flue gas of the gas-fired boiler in the energy station to be monitored is uploaded to a combined learning Internet of things platform.
In some optional implementation manners of some embodiments, in response to that the oxygen content of the flue gas of the target gas-fired boiler is greater than a preset threshold, an alarm audio is sent to a terminal device with a voice function corresponding to the target gas-fired boiler, and the alarm audio is played.
The method for predicting the oxygen content in the flue gas of the gas-fired boiler disclosed by some embodiments of the present disclosure includes firstly, acquiring a flue gas oxygen content prediction model request of a target gas-fired boiler in an energy station to be monitored and operation data of the target gas-fired boiler, wherein the flue gas oxygen content prediction model request carries a gas-fired boiler identifier; then, determining a target combined energy station according to the gas boiler identifier; then, establishing a combined flue gas oxygen content prediction model according to the gas boiler operation data corresponding to each target combined energy station; and finally, generating the oxygen content of the flue gas of the target gas-fired boiler according to the combined flue gas oxygen content prediction model and the operation data of the target gas-fired boiler. In conclusion, according to the technical scheme of the invention, the oxygen content of the flue gas can be monitored in real time without using a sensor to measure the oxygen content of the flue gas, the maintenance is convenient, the measurement error is reduced, meanwhile, the combined flue gas oxygen content prediction model depends on real data, the influence of the external environment is not easy to influence, and the oxygen content of the flue gas can be more accurately determined.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 1 shows only a basic embodiment of the method of the present invention, and based on this, certain optimization and expansion can be performed, and other preferred embodiments of the method can also be obtained.
Fig. 2 shows another embodiment of the method for predicting oxygen content in flue gas of a gas boiler according to the present invention. On the basis of the foregoing embodiments, the present embodiment is described in more detail with reference to application scenarios. It should be understood that the method described in this embodiment is also applicable in other relevant scenarios.
The specific scenario combined in this embodiment is as follows: as shown in fig. 5, it is assumed that there are 3 energy stations (a plurality of gas boilers in the energy stations are similar) and a joint learning internet of things platform T, where the 3 energy stations are a joint energy station a, a joint energy station B, and an energy station C to be monitored, and the joint learning internet of things platform T interacts with a joint learning client Ac, a joint learning client Bc, and a monitoring client Cc, where the joint energy stations a and B have rich data, and a local flue gas oxygen content prediction model, that is, a contributor of the joint learning model, can be trained using operating data of their gas boilers; the gas boiler in the energy station C to be monitored has no smoke oxygen content measuring point; the combined learning client Ac stores the operation data of the gas boilers A1, A2 and A3 in the combined energy station A, the combined learning client Bc stores the operation data of the gas boilers B1, B2 and B3 in the combined energy station B, and the monitoring client Cs stores the operation data of the gas boilers C1, C2 and C3 in the energy station C to be monitored; 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, wherein the local operation and maintenance servers refer to servers for operation and maintenance of the gas-fired boiler. The method aims to establish a combined flue gas oxygen content prediction model for predicting the flue gas oxygen content of the gas boiler of the energy station to be monitored by combining the gas boiler operation data in the combined learning client.
The method in this embodiment includes the following steps:
And the energy station C to be monitored initiates a demand application of a flue gas oxygen content prediction model of the gas boiler, namely a flue gas oxygen content prediction model request to the joint learning Internet of things platform T through a monitoring client Cc and an API (application programming interface) interface deployed on the local operation and maintenance server Cs.
The platform T of the joint learning Internet of things analyzes the identifier of the gas boiler to obtain description information of the gas boiler in the energy station C to be monitored, wherein the description information is the model of the gas boiler C1, the models of the gas boilers C1, C2 and C3 are the same, the model of the gas boiler A1 in the joint energy station A is obtained, the models of the gas boilers A1, A2 and A3 are the same, the model of the gas boiler B1 in the joint energy station B is the same, and the models of the gas boilers B1, B2 and B3 are the same.
The model of the gas boiler A1 in the combined energy station A is the same as that of the gas boiler C1, the similarity between the energy station C to be monitored and the combined energy station A is 1, the model of the gas boiler B1 in the combined energy station B is the same as that of the gas boiler C1, and the similarity between the energy station C to be monitored and the combined energy station B is 1.
And 204, determining a reference combined energy station from each candidate combined energy station according to the similarity between each candidate combined energy station and the energy station to be monitored.
And respectively determining the combined energy station A and the combined energy station B as reference combined energy stations.
And the joint learning Internet of things platform T sends joint learning invitations to the joint learning client Ac and the joint learning client Bc, and if the joint learning client Ac and the joint learning client Bc return agreement, the joint energy station A and the joint energy station B are respectively determined as target joint energy stations.
And step 206, obtaining a local flue gas oxygen content prediction model of each joint learning client, and constructing a joint flue gas oxygen content prediction model according to the local flue gas oxygen content prediction model of each joint learning client.
The joint learning client Ac performs model training on the model to be trained on the basis of the operation data of the gas boilers A1, A2 and A3, model parameters are uploaded to a joint learning Internet of things platform T through an API (application programming interface), a joint learning client Bc performs model training on a model to be trained based on the operation data of the gas boilers B1, B2 and B3, and uploading the model parameters to a joint learning Internet of things platform T, issuing the aggregated model parameters to a joint learning client Ac and a joint learning client Bc by the joint learning Internet of things platform T for model iteration, obtaining local flue gas oxygen content prediction models by the joint learning client Ac and the joint learning client Bc respectively, and fusing the local flue gas oxygen content prediction models obtained by the joint learning client Ac and the joint learning client Bc by the joint learning Internet of things platform T to obtain a joint flue gas oxygen content prediction model.
And step 207, calling the combined flue gas oxygen content prediction model in the model database, and sending the combined flue gas oxygen content prediction model to a client corresponding to the energy station to be monitored, so that the client can download the combined flue gas oxygen content prediction model, and predict the flue gas oxygen content of the gas boiler through the downloaded combined flue gas oxygen content prediction model.
And the joint learning Internet of things platform T issues the joint flue gas oxygen content prediction model to the monitoring client Cc, the monitoring client Cc downloads the joint flue gas oxygen content prediction model, and the model is used for predicting the flue gas oxygen contents of the gas-fired boilers C1, C2 and C3.
According to the technical scheme, the beneficial effects of the embodiment are as follows: based on the interaction between the platform of the joint learning Internet of things and the client of the gas boiler, model calling is realized, so that the oxygen content of the flue gas of the gas boiler can be rapidly, accurately and in real time predicted.
FIG. 3 is a schematic view of a flue gas oxygen content prediction device of a gas boiler according to an embodiment of the present invention; the above-mentioned flue gas oxygen content prediction device 300 of gas boiler includes: 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 oxygen content prediction model request of a target gas-fired boiler in an energy station to be monitored and operation data of the target gas-fired boiler, wherein the flue gas oxygen content prediction model request carries a gas-fired boiler identifier; a determining module 302 configured to determine a target combined energy station according to the gas boiler identification; a building module 303 configured to build a combined flue gas oxygen content prediction model according to the gas boiler operation data corresponding to each target combined energy station; and a generating module 304 configured to generate the flue gas oxygen content of the target gas boiler according to the combined flue gas oxygen content prediction model and the operation data of the target gas boiler.
In some optional implementations of some embodiments, the determining module 302 in the flue gas oxygen content prediction device 300 of the gas boiler comprises: the acquisition unit is configured to acquire the description information of the gas boiler in the energy station to be monitored and the description information of the candidate gas boiler in the candidate combined energy station according to the gas boiler identification; a first determining unit configured to determine, for each of the candidate combined energy stations, a similarity between the energy station to be monitored and the candidate combined energy station according to the description information of the gas boilers in the energy station to be monitored and the description information of the candidate gas boilers in the candidate combined energy station; and the second determining unit is configured to determine a target combined energy station according to the similarity between each candidate combined energy station and the energy station to be monitored.
In some optional implementations of some embodiments, the second determining unit in the flue gas oxygen content prediction device 300 of the gas boiler is further configured to: determining a reference combined energy station from each candidate combined energy station according to the similarity between each candidate combined energy station and the energy station to be monitored; respectively sending joint learning invitation to joint learning clients corresponding to the reference joint energy stations; and respectively determining all the reference joint energy stations agreeing with the joint learning invitation as target joint energy stations.
In some optional implementations of some embodiments, the building module 303 in the flue gas oxygen content prediction device 300 of the gas boiler is further configured to: obtaining a model to be trained, and sending the model to be trained to the joint learning client corresponding to each target joint energy station; acquiring a local flue gas oxygen content prediction model of each joint learning client, wherein the local flue gas oxygen content prediction model is obtained by joint learning based on gas boiler operation data in the joint learning client and the model to be trained; and constructing a combined flue gas oxygen content prediction model according to the respective local flue gas oxygen content prediction model of each combined learning client.
In some optional implementations of some embodiments, the request of the flue gas oxygen content prediction model is sent by a client corresponding to the energy station to be monitored; predicting the flue gas oxygen content of the gas-fired boiler in the energy station to be monitored according to the combined flue gas oxygen content prediction model, and further configuring the flue gas oxygen content of the gas-fired boiler into: and calling the combined flue gas oxygen content prediction model in the model database, and sending the combined flue gas oxygen content prediction model to a client corresponding to the energy station to be monitored so that the client can download the combined flue gas oxygen content prediction model, and predicting the flue gas oxygen content of the gas boiler through the downloaded combined flue gas oxygen content prediction model.
In some optional implementations of some embodiments, the above flue gas oxygen content prediction device 300 of the gas boiler is further configured to: adding the combined flue gas oxygen content prediction model into a model database; and when the request of the flue gas oxygen content prediction model is received again, calling the combined flue gas oxygen content prediction model in the model database, and sending the combined flue gas oxygen content prediction model to the client corresponding to the energy station to be monitored.
In some optional implementations of some embodiments, the above flue gas oxygen content prediction device 300 of the gas boiler is further configured to: and responding to the fact that the oxygen content of the flue gas of the target gas-fired boiler is larger than a preset threshold value, sending alarm audio to the terminal equipment with the voice function corresponding to the target gas-fired boiler, and playing the alarm audio.
It will be understood that the units described in the apparatus 300 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 300 and the units included therein, and are not described herein again.
Fig. 4 is a schematic diagram of a flue gas oxygen content prediction apparatus/terminal device of a gas boiler according to an embodiment of the present invention. As shown in fig. 4, the flue gas oxygen content prediction apparatus/terminal device 4 of the gas boiler of this embodiment includes: a processor 40, a memory 41 and a computer program 42 stored in said memory 41 and executable on said processor 40. The processor 40, when executing the computer program 42, implements the steps of the above-mentioned method for predicting the oxygen content in flue gas of each gas boiler, such as the steps 101 to 103 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, implements the functions of the modules/units in the device embodiments, such as the modules 401 to 404 shown in fig. 4.
Illustratively, the computer program 42 may be partitioned into one or more modules/units that are stored in the memory 41 and executed by the processor 40 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution process of the computer program 42 in the flue gas oxygen content prediction device/terminal equipment 4 of the gas boiler. For example, the computer program 42 may be divided into a synchronization module, a summary module, an acquisition module, and a return module (a module in a virtual device), and each module has the following specific functions:
the flue gas oxygen content prediction device/terminal device 4 of the gas boiler can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing devices. The flue gas oxygen content prediction device/terminal equipment of the gas boiler can include, but is not limited to, a processor 40 and a memory 41. It will be understood by those skilled in the art that fig. 4 is only an example of the flue gas oxygen content prediction device/terminal equipment 4 of the gas boiler, and does not constitute a limitation of the flue gas oxygen content prediction device/terminal equipment 4 of the gas boiler, and may include more or less components than those shown, or combine some components, or different components, for example, the flue gas oxygen content prediction device/terminal equipment of the gas boiler may further include an input and output device, a network access device, a bus, etc.
The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the flue gas oxygen content prediction device/terminal device 4 of the gas boiler, for example, a hard disk or a memory of the flue gas oxygen content prediction device/terminal device 4 of the gas boiler. The memory 41 may also be an external storage device of the flue gas oxygen content prediction device/terminal device 4 of the gas boiler, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, which is equipped on the flue gas oxygen content prediction device/terminal device 4 of the gas boiler. Further, the memory 41 may also include both an internal storage unit and an external storage device of the flue gas oxygen content prediction apparatus/terminal device 4 of the gas boiler. The memory 41 is used for storing the computer program and other programs and data required by the flue gas oxygen content prediction device/terminal equipment of the gas boiler. The memory 41 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method for predicting the oxygen content of flue gas of a gas boiler is characterized by comprising the following steps:
acquiring a flue gas oxygen content prediction model request of a target gas-fired boiler in an energy station to be monitored and operation data of the target gas-fired boiler, wherein the flue gas oxygen content prediction model request carries a gas-fired boiler identifier;
determining a target combined energy station according to the gas boiler identifier;
establishing a combined flue gas oxygen content prediction model according to the gas-fired boiler operation data corresponding to each target combined energy station;
and generating the oxygen content of the flue gas of the target gas-fired boiler according to the combined flue gas oxygen content prediction model and the operation data of the target gas-fired boiler.
2. The method for predicting the oxygen content in flue gas of a gas boiler as claimed in claim 1, wherein the determining a target combined energy station according to the identity of the gas boiler comprises:
according to the gas boiler identification, obtaining description information of the gas boiler in the energy station to be monitored and description information of candidate gas boilers in the candidate combined energy station;
for each candidate combined energy station, determining the similarity between the energy station to be monitored and the candidate combined energy station according to the description information of the gas boilers in the energy station to be monitored and the description information of the candidate gas boilers in the candidate combined energy station;
and determining a target combined energy station according to the similarity between each candidate combined energy station and the energy station to be monitored.
3. The method for predicting the oxygen content in the flue gas of the gas-fired boiler according to claim 2, wherein the step of determining the target combined energy station according to the similarity between each candidate combined energy station and the energy station to be monitored comprises the following steps:
determining a reference combined energy station from each candidate combined energy station according to the similarity between each candidate combined energy station and the energy station to be monitored;
respectively sending joint learning invitation to joint learning clients corresponding to the reference joint energy stations;
and respectively determining all the reference joint energy stations agreeing with the joint learning invitation as target joint energy stations.
4. The method for predicting the oxygen content in flue gas of a gas-fired boiler according to claim 1, wherein the step of constructing a joint flue gas oxygen content prediction model according to the operation data of the gas-fired boiler corresponding to each target joint energy station comprises the following steps:
obtaining a model to be trained, and sending the model to be trained to the joint learning client corresponding to each target joint energy station;
acquiring a local flue gas oxygen content prediction model of each joint learning client, wherein the local flue gas oxygen content prediction model is obtained by joint learning based on gas boiler operation data in the joint learning client and the model to be trained;
and constructing a combined flue gas oxygen content prediction model according to the respective local flue gas oxygen content prediction model of each combined learning client.
5. The method for predicting the oxygen content in the flue gas of the gas-fired boiler according to claim 1, wherein the request of the prediction model of the oxygen content in the flue gas is sent by a client corresponding to the energy station to be monitored;
the predicting the oxygen content of the flue gas of the gas-fired boiler in the energy station to be monitored according to the combined flue gas oxygen content prediction model comprises the following steps:
and calling the combined flue gas oxygen content prediction model in the model database, and sending the combined flue gas oxygen content prediction model to a client corresponding to the energy station to be monitored so that the client can download the combined flue gas oxygen content prediction model, and predicting the flue gas oxygen content of the gas boiler through the downloaded combined flue gas oxygen content prediction model.
6. The method for predicting oxygen content in flue gas of a gas boiler according to claim 1, wherein said method further comprises:
adding the combined flue gas oxygen content prediction model into a model database;
and when the request of the flue gas oxygen content prediction model is received again, calling the combined flue gas oxygen content prediction model in the model database, and sending the combined flue gas oxygen content prediction model to the client corresponding to the energy station to be monitored.
7. The method for predicting oxygen content in flue gas of a gas boiler according to claim 1, wherein said method further comprises:
and responding to the fact that the oxygen content of the flue gas of the target gas-fired boiler is larger than a preset threshold value, sending alarm audio to the terminal equipment with the voice function corresponding to the target gas-fired boiler, and playing the alarm audio.
8. A flue gas oxygen content prediction device of a gas boiler is characterized by comprising:
the system comprises an acquisition module, a monitoring module and a monitoring module, wherein the acquisition module is configured to acquire a flue gas oxygen content prediction model request of a target gas boiler in an energy station to be monitored and operation data of the target gas boiler, and the flue gas oxygen content prediction model request carries a gas boiler identifier;
a determination module configured to determine a target combined energy station based on the gas boiler identification;
the construction module is configured to construct a joint flue gas oxygen content prediction model according to the gas-fired boiler operation data corresponding to each target joint energy station;
a generating module configured to generate the flue gas oxygen content of the target gas boiler according to the combined flue gas oxygen content prediction model and the operational data of the target gas boiler.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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