CN114118542A - Method and device for selecting flue gas oxygen content load prediction model - Google Patents
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- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 title claims abstract description 44
- 239000001301 oxygen Substances 0.000 title claims abstract description 44
- 229910052760 oxygen Inorganic materials 0.000 title claims abstract description 44
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 title claims abstract description 40
- 239000003546 flue gas Substances 0.000 title claims abstract description 40
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Abstract
The invention discloses a method and a device for selecting a flue gas oxygen content load prediction model. The method comprises the following steps: receiving a training data set and a testing data set from a prediction device of a participant based on a joint learning architecture; preprocessing data in a training data set and data in a testing data set of the prediction equipment to obtain a preprocessed equipment data set; calculating an evaluation index value of each piece of data in the preprocessed equipment data set according to the establishment of the prediction model group; and determining a flue gas oxygen content load prediction model suitable for the prediction equipment according to the minimum evaluation index value. The invention improves the prediction precision of the oxygen content of the flue gas of the energy equipment and reduces the measurement cost of the prior art.
Description
Technical Field
The disclosure relates to the technical field of energy, in particular to a selection method and device of a flue gas oxygen content load prediction model.
Background
At present, with the rapid development of industrial technologies, more and more complex industrial devices and industrial networks have been formed in the energy industry. In such a complex and developed industrial environment, the measurement of data on energy equipment requires a lot of manpower and material resources, such as the prediction of the flue gas oxygen content load of a boiler or the prediction of the flue gas oxygen content load of a steam engine. In the prior art, each data type is analyzed and then tested by one-by-one algorithm, so that the problems of overlong time cost and untimely prediction of data prediction precision are caused.
Disclosure of Invention
In view of this, the embodiments of the present disclosure provide a method and an apparatus for selecting a flue gas oxygen content load prediction model, so as to solve the problems in the prior art that the prediction of energy equipment is not timely and the prediction of the flue gas oxygen content of the energy equipment is not accurate.
In a first aspect of the embodiments of the present disclosure, a method for selecting a flue gas oxygen content load prediction model is provided, including:
receiving a training data set and a testing data set from a prediction device of a participant based on a joint learning architecture;
preprocessing data in a training data set and data in a testing data set of the prediction equipment to obtain a preprocessed equipment data set;
calculating an evaluation index value of each piece of data in the preprocessed equipment data set according to the establishment of the prediction model group;
and determining a flue gas oxygen content load prediction model suitable for the prediction equipment according to the minimum evaluation index value.
In a second aspect of the embodiments of the present disclosure, a selection device of a flue gas oxygen content load prediction model is provided, which includes:
a receiving module for receiving a training data set and a testing data set from a prediction device of a participant based on a joint learning architecture;
the device comprises a preprocessing module, a data processing module and a data processing module, wherein the preprocessing module is used for preprocessing data in a training data set and data in a testing data set of the prediction device and obtaining a preprocessed device data set;
the calculation module is used for calculating the evaluation index value of each piece of data in the preprocessed equipment data set according to the established prediction model group;
and the prediction module is used for determining a flue gas oxygen content load prediction model suitable for the prediction equipment according to the minimum evaluation index value.
In a third aspect of the embodiments of the present disclosure, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.
In a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, which stores a computer program, which when executed by a processor, implements the steps of the above-mentioned method.
Compared with the prior art, the embodiment of the disclosure has the following beneficial effects: receiving a training data set and a testing data set from a prediction device of a participant by a joint learning architecture; preprocessing data in a training data set and data in a testing data set of the prediction equipment to obtain a preprocessed equipment data set; calculating an evaluation index value of each piece of data in the preprocessed equipment data set according to the establishment of the prediction model group; and determining a flue gas oxygen content load prediction model suitable for the prediction equipment according to the minimum evaluation index value. So as to improve the prediction of the oxygen content of the flue gas of the energy equipment and reduce the measurement cost of the prior art.
Drawings
To more clearly illustrate the technical solutions in the embodiments of the present disclosure, 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 disclosure, and other drawings can be obtained by those skilled in the art without inventive efforts.
FIG. 1 is a scenario diagram of an application scenario of an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart illustrating a method for selecting a flue gas oxygen content load prediction model according to an embodiment of the present disclosure;
FIG. 3 is a schematic structural diagram of a selection device of a flue gas oxygen content load prediction model according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure.
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 disclosed embodiments. However, it will be apparent to one skilled in the art that the present disclosure 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 disclosure with unnecessary detail.
Joint learning refers to comprehensively utilizing multiple AI (Artificial Intelligence) technologies on the premise of ensuring data security and user privacy, jointly mining data values by combining multiple parties, and promoting new intelligent business states and modes based on joint modeling. The joint learning has at least the following characteristics:
(1) and the participating nodes control the weak centralized joint training mode of own data, so that the data privacy security in the co-creation intelligent process is ensured.
(2) Under different application scenes, a plurality of model aggregation optimization strategies are established by utilizing screening and/or combined AI algorithm and privacy protection calculation so as to obtain a high-level and high-quality model.
(3) On the premise of ensuring data security and user privacy, the method for improving the efficiency of the joint learning engine is obtained based on a plurality of model aggregation optimization strategies, wherein the efficiency method can improve the overall efficiency of the joint learning engine by solving the problems of information interaction, intelligent perception, abnormal processing mechanisms and the like under the conditions of parallel computing architectures and large-scale cross-domain networks.
(4) The requirements of the users of multiple parties in each scene are acquired, the real contribution degree of each joint participant is determined and reasonably evaluated through a mutual trust mechanism, and distribution stimulation is carried out.
Based on the mode, the AI technical ecology based on the joint learning can be established, the industrial data value is fully exerted, and the falling of scenes in the vertical field is promoted.
The following describes a selection method and device of a flue gas oxygen content load prediction model according to an embodiment of the present disclosure in detail with reference to the accompanying drawings.
Fig. 1 is a scenario diagram of an application scenario of an embodiment of the present disclosure, which illustrates a joint learning architecture. As shown in fig. 1, the architecture of joint learning may include a server (central node) 101, as well as a participant 102, a participant 103, and a participant 104. Wherein, the participant can be one or more client components.
In the joint learning process, a basic model may be built by the server 101, and the server 101 sends the model to the participants 102, 103, and 104 with which communication connections are established. The basic model may also be uploaded to the server 101 after any participant has established the model, and the server 101 sends the model to other participants with whom communication connection is established. The participating party 102, the participating party 103 and the participating party 104 construct models according to the downloaded basic structures and model parameters, perform model training by using local data to obtain updated model parameters, and upload the updated model parameters to the server 101 in an encrypted manner. Server 101 aggregates the model parameters sent by participants 102, 103, and 104 to obtain global model parameters, and passes the global model parameters back to participants 102, 103, and 104. And the participants 102, 103 and 104 iterate the respective models according to the received global model parameters until the models finally converge, thereby realizing the training of the models. In the joint learning process, data uploaded by the participants 102, 103 and 104 are model parameters, local data are not uploaded to the server 101, and all the participants can share the final model parameters, so that common modeling can be realized on the basis of ensuring data privacy.
It should be noted that the number of the participants is not limited to three as described above, but may be set according to needs, which is not limited by the embodiment of the present disclosure.
Fig. 2 is a schematic flow chart of a method for selecting a flue gas oxygen content load prediction model according to an embodiment of the present disclosure. The method for selecting the flue gas oxygen content load prediction model of fig. 2 may be performed by the server of fig. 1. As shown in fig. 2, the selection method of the flue gas oxygen content load prediction model includes:
s201, based on a joint learning framework, a training data set and a testing data set of prediction equipment from a participant are received.
The training data set can be models of different energy equipment (for example, different boiler models), and the test set data can be flue gas oxygen content data of the energy equipment under different processes and corresponding characteristic data thereof.
Specifically, the attribute of the prediction equipment is determined to correspond to the flue gas oxygen content data of the prediction equipment according to the attribute of the prediction equipment; then, extracting and predicting the characteristics of the oxygen content data of the smoke of the equipment; and further, respectively establishing a training data set and a testing data set of the prediction equipment by utilizing the characteristics of the flue gas oxygen content data of the prediction equipment.
S202, preprocessing the data in the training data set and the data in the testing data set of the prediction device to obtain a preprocessed device data set.
Specifically, whether the data in the training data set and the test data set of the prediction device are abnormal or not can be judged; and if the abnormality exists, performing abnormality processing on the data in the training data set and the test data set of the prediction equipment. And then, performing data normalization processing on the data in the training data set and the data in the test data set of the prediction equipment after the abnormal processing.
S203, according to the set of the established prediction model, the evaluation index value of each piece of data in the preprocessed equipment data set is calculated.
The prediction model group may be composed of algorithms such as a selected xgboost algorithm, an SVR algorithm, a neural network algorithm, a belief network algorithm, a decision tree algorithm, a random forest regression algorithm, a gradient lifting tree regression algorithm, a linear regression algorithm, a deep learning algorithm, and the like, and the invention is not limited thereto.
Specifically, a prediction model group may be established according to the attribute of the prediction device and each piece of data in the device data set after the prediction preprocessing; then, respectively calculating the root mean square error of each piece of data in the training set and the test set by using the prediction model group; and then, the root mean square error of each piece of data in the training set and the test set can be used as an evaluation index value of each piece of data in the preprocessed equipment data set.
Further, as for the implementation manner of respectively calculating the root mean square error of each piece of data in the training set and the test set by using the prediction model group, preferably, the algorithm in the prediction model group can be trained by using the training data set of the prediction device to obtain a prediction result; the algorithms in the prediction model set may be trained using a test data set of a prediction device to obtain a test result; and further, according to the prediction result and the test result, obtaining the root mean square error of each piece of data in the training set and the test set.
For example, calculating each data of the training set and the test set to predict, obtaining the root mean square error of each data (set as rmse), taking the root mean square error as the evaluation index value of each data in the preprocessed equipment data set, and then selecting the algorithm with the minimum rmse value as the corresponding algorithm label of the training set and the test set (labeling each algorithm in the algorithm group, the label value is 1,2,3, etc.)
Regarding the process of rmse derivation in step: and training the algorithms given by the algorithm group by using the training set, testing the prediction result obtained by the training algorithm by using the test set, and obtaining the rmse index by using the prediction result and the test set. The rmse index calculation formula is as follows:
wherein n is more than or equal to 1 and is a label value; y isiIn order to be a training set, the training set,
is a test set; and i is more than or equal to 1 and is the number of the corresponding data set.
And S204, determining a flue gas oxygen content load prediction model suitable for the prediction equipment according to the minimum evaluation index value.
Specifically, the evaluation index values of each piece of data in the preprocessed device data set can be sorted from small to large; selecting the minimum evaluation index value according to the sorting result; then, calling a label value corresponding to a prediction model in the prediction model group;
and when the minimum evaluation index value is matched with the label value corresponding to the prediction model in the prediction model group, determining the prediction model corresponding to the label value corresponding to the prediction model as the flue gas oxygen content load prediction model suitable for the prediction equipment.
For the mode of calling the label value corresponding to the prediction model in the prediction model group, clustering the training data set of the prediction device by using a classification algorithm to obtain at least two types of training clustering data; then, classifying at least two types of training clustered data by calling a classifier; training at least two classifiers corresponding to the at least two training clusters according to the classified data of the at least two training clusters; and performing cluster prediction on the test data of the prediction equipment to obtain at least one of at least two types of training clusters. Determining a classifier corresponding to at least one category in the at least two types of training clusters according to the at least one category in the at least two types of training clusters; and finally, determining the label value corresponding to the prediction model in the prediction model group according to the class label value corresponding to the classifier.
Further exemplifying: the training set data may be clustered by using a binary algorithm to obtain K classes (K is a constant) of the number of classes, then a classification algorithm is selected to perform K classes to obtain corresponding data in the K classes, a classifier is selected, for example, a gradient lifting regression tree is selected to classify the K classes of data, and labels (given by step S203) corresponding to the training data have the K classes of data, so that the K classifiers are trained. Then, carrying out cluster prediction operation on the test set data, wherein the prediction result is a certain class in the 1-K clusters, then carrying out classification operation by using a classifier corresponding to the class to obtain an output result, the result corresponds to a certain prediction model in the algorithm group, then carrying out data prediction by using the algorithm, and further judging the correctness of the prediction model.
According to the technical scheme provided by the embodiment of the disclosure, a training data set and a testing data set of prediction equipment from a participant are received on the basis of a joint learning architecture; preprocessing data in a training data set and data in a testing data set of the prediction equipment to obtain a preprocessed equipment data set; calculating an evaluation index value of each piece of data in the preprocessed equipment data set according to the establishment of the prediction model group; and determining a flue gas oxygen content load prediction model suitable for the prediction equipment according to the minimum evaluation index value. So as to improve the prediction of the oxygen content of the flue gas of the energy equipment and reduce the measurement cost of the prior art.
All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described herein again.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 3 is a schematic diagram of a selection device of a flue gas oxygen content load prediction model according to an embodiment of the disclosure. As shown in fig. 3, the selection device of the flue gas oxygen content load prediction model comprises:
a receiving module 301, configured to receive a training data set and a testing data set of a prediction device from a participant based on a joint learning architecture;
a preprocessing module 302, configured to preprocess data in a training data set and data in a test data set of the prediction device, and obtain a preprocessed device data set;
a calculating module 303, configured to calculate an evaluation index value of each piece of data in the preprocessed device data set according to the set of prediction models;
and the prediction module 304 is used for determining a flue gas oxygen content load prediction model suitable for the prediction equipment according to the minimum evaluation index value.
According to the technical scheme provided by the embodiment of the disclosure, a training data set and a testing data set of prediction equipment from a participant are received on the basis of a joint learning architecture; preprocessing data in a training data set and data in a testing data set of the prediction equipment to obtain a preprocessed equipment data set; calculating an evaluation index value of each piece of data in the preprocessed equipment data set according to the establishment of the prediction model group; and determining a flue gas oxygen content load prediction model suitable for the prediction equipment according to the minimum evaluation index value. So as to improve the prediction of the oxygen content of the flue gas of the energy equipment and reduce the measurement cost of the prior art.
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 on the implementation process of the embodiments of the present disclosure.
Fig. 4 is a schematic diagram of an electronic device 4 provided by the embodiment of the present disclosure. As shown in fig. 4, the electronic apparatus 4 of this embodiment includes: a processor 401, a memory 402 and a computer program 403 stored in the memory 402 and executable on the processor 401. The steps in the various method embodiments described above are implemented when the processor 401 executes the computer program 403. Alternatively, the processor 401 implements the functions of the respective modules/units in the above-described respective apparatus embodiments when executing the computer program 403.
Illustratively, the computer program 403 may be partitioned into one or more modules/units, which are stored in the memory 402 and executed by the processor 401 to accomplish the present disclosure. One or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 403 in the electronic device 4.
The electronic device 4 may be a desktop computer, a notebook, a palm computer, a cloud server, or other electronic devices. The electronic device 4 may include, but is not limited to, a processor 401 and a memory 402. Those skilled in the art will appreciate that fig. 4 is merely an example of the electronic device 4, and does not constitute a limitation of the electronic device 4, and may include more or less components than those shown, or combine certain components, or different components, e.g., the electronic device may also include input-output devices, network access devices, buses, etc.
The Processor 401 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 device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 402 may be an internal storage unit of the electronic device 4, for example, a hard disk or a memory of the electronic device 4. The memory 402 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device 4. Further, the memory 402 may also include both internal storage units of the electronic device 4 and external storage devices. The memory 402 is used for storing computer programs and other programs and data required by the electronic device. The memory 402 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, so as to perform all or part of the functions described above. 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 disclosure.
In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other ways. For example, the above-described apparatus/electronic device embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, and multiple units or components may be combined or integrated into another system, or some features may be omitted or not implemented. 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.
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 disclosure 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, the present disclosure may implement all or part of the flow of the method in the above embodiments, and may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the above methods and embodiments. The computer program may comprise 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 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 suitable additions or additions that may be required in accordance with legislative and patent practices within the jurisdiction, for example, in some jurisdictions, computer readable media may not include electrical carrier signals or telecommunications signals in accordance with legislative and patent practices.
The above examples are only intended to illustrate the technical solutions of the present disclosure, not to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should 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 disclosure, and are intended to be included within the scope of the present disclosure.
Claims (10)
1. A selection method of a flue gas oxygen content load prediction model is characterized by comprising the following steps:
receiving a training data set and a testing data set from a prediction device of a participant based on a joint learning architecture;
preprocessing data in a training data set and data in a testing data set of the prediction equipment to obtain a preprocessed equipment data set;
calculating an evaluation index value of each piece of data in the preprocessed equipment data set according to the established prediction model group;
and determining a flue gas oxygen content load prediction model suitable for the prediction equipment according to the minimum evaluation index value.
2. The method of claim 1, wherein receiving a training data set and a testing data set from a predictive device of a participant comprises:
determining the attribute of the prediction equipment corresponding to the smoke oxygen content data of the prediction equipment according to the attribute of the prediction equipment;
extracting and predicting the characteristics of the oxygen content data of the smoke of the equipment;
and respectively establishing a training data set and a testing data set of the prediction equipment by utilizing the characteristics of the flue gas oxygen content data of the prediction equipment.
3. The method of claim 1, wherein preprocessing data in a training dataset and data in a testing dataset of a predictive device and obtaining a preprocessed device dataset comprises:
judging whether the data in the training data set and the test data set of the prediction equipment are abnormal or not;
if the abnormal data exists, performing abnormal processing on the data in the training data set and the test data set of the prediction equipment;
and carrying out data normalization processing on the data in the training data set and the data in the test data set of the prediction equipment after exception processing.
4. The method of claim 1, wherein calculating an evaluation index value for each piece of data in the pre-processed device data set based on the set of established predictive models comprises:
establishing a prediction model group according to the attribute of the prediction equipment and each piece of data in the equipment data set after prediction pretreatment;
respectively calculating the root mean square error of each piece of data in the training set and the test set by using the prediction model group;
and taking the root mean square error of each piece of data of the obtained training set and the test set as an evaluation index value of each piece of data in the preprocessed equipment data set.
5. The method of claim 4, wherein calculating the root mean square error for each of the data in the training set and the test set using the set of predictive models comprises:
training the algorithm in the prediction model group by using a training data set of prediction equipment to obtain a prediction result;
training an algorithm in the prediction model group by using a test data set of prediction equipment to obtain a test result;
and obtaining the root mean square error of each piece of data in the training set and the test set according to the prediction result and the test result.
6. The method of claim 1, wherein determining a flue gas oxygen content load prediction model suitable for a prediction device based on the minimum evaluation index value comprises:
ranking the evaluation index values of each piece of data in the preprocessed equipment data set from small to large;
selecting the minimum evaluation index value according to the sorting result;
calling a label value corresponding to a prediction model in the prediction model group;
and when the minimum evaluation index value is matched with the tag value corresponding to the prediction model in the prediction model group, determining the prediction model corresponding to the tag value as the smoke oxygen content load prediction model suitable for the prediction equipment.
7. The method of claim 4, wherein retrieving the label values corresponding to the prediction models in the set of prediction models comprises:
clustering the training data set of the prediction equipment by using a classification algorithm to obtain at least two types of training clustering data;
calling a classifier, and classifying the data of the at least two types of training clusters;
training at least two classifiers corresponding to the at least two training clusters according to the classified data of the at least two training clusters;
performing cluster prediction on test data of prediction equipment to obtain at least one category of the at least two categories of training clusters;
determining a classifier corresponding to at least one category in the at least two types of training clusters according to the at least one category in the at least two types of training clusters;
and determining the label value corresponding to the prediction model in the prediction model group according to the class label value corresponding to the classifier.
8. A selection device of a flue gas oxygen content load prediction model is characterized by comprising:
a receiving module for receiving a training data set and a testing data set from a prediction device of a participant based on a joint learning architecture;
the device comprises a preprocessing module, a data processing module and a data processing module, wherein the preprocessing module is used for preprocessing data in a training data set and data in a testing data set of the prediction device and obtaining a preprocessed device data set;
the calculation module is used for calculating the evaluation index value of each piece of data in the preprocessed equipment data set according to the established prediction model group;
and the prediction module is used for determining a flue gas oxygen content load prediction model suitable for the prediction equipment according to the minimum evaluation index value.
9. An electronic 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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| PCT/CN2022/116583 WO2023082788A1 (en) | 2021-11-11 | 2022-09-01 | Method and apparatus for predicting oxygen content in flue gas and load, method and apparatus for selecting prediction model, and method and apparatus for predicting flue gas emission |
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