CN114118540A - Flue gas oxygen content load prediction method and device based on sample migration - Google Patents
Flue gas oxygen content load prediction method and device based on sample migration Download PDFInfo
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Abstract
The invention discloses a method and a device for predicting flue gas oxygen content load based on sample migration. The method comprises the following steps: acquiring device data of a first participant and a second participant under a joint learning architecture; the first participant is a participant who puts forward a forecast demand, and the second participant is other participants except the first participant; training a predictive classifier using the device data of the first participant and the device data of the second participant; determining, from the predictive classifier, weight data for the device data of the first participant in relation to the device data of the second participant; training a predictive gradient boost model based on the device data and the weight data of the second participant; and predicting the oxygen content load of the smoke of the first participant device by using a prediction type gradient lifting model. The invention solves the problem of inaccurate prediction of the oxygen content load of the flue gas caused by the data distribution difference of the energy equipment generated under different processes, and saves the cost of the energy equipment sensor.
Description
Technical Field
The disclosure relates to the technical field of energy, and in particular relates to a method and a device for predicting flue gas oxygen content load based on sample migration, computer equipment and a computer-readable storage medium.
Background
The application of comprehensive energy is indispensable in the current society. With the wide application, the application of energy equipment is required to be higher and higher, but in industrial application, many large-scale energy equipment cannot be updated at any time, or the overproof of heat load in application is not easy to be checked.
For example, in industrial energy applications, the distribution difference of relevant equipment data generated by boiler equipment under different processes may be very large, which often results in low prediction accuracy of the oxygen content in flue gas, and is not favorable for operations such as early warning and scheduling of later-stage equipment. It is imperative that we solve these problems.
Disclosure of Invention
In view of this, the embodiments of the present disclosure provide a method, an apparatus, a computer device, and a computer readable storage medium for predicting a flue gas oxygen content load based on sample migration, so as to solve the problem in the prior art that the flue gas oxygen content load prediction is inaccurate due to data distribution differences of energy devices generated under different processes.
In a first aspect of the embodiments of the present disclosure, a method for predicting a load of oxygen content in flue gas based on sample migration is provided, and is applied to a joint learning framework, including:
acquiring device data of a first participant and device data of a second participant under a joint learning architecture; the first participant is a participant who puts forward a forecast demand, and the second participant is other participants except the first participant;
training a predictive classifier using the device data of the first participant and the device data of the second participant;
determining, from the predictive classifier, weight data for the device data of the first participant in relation to the device data of the second participant;
training a predictive gradient boost model based on the device data and the weight data of the second participant;
and predicting the oxygen content load of the smoke of the first participant device by using a prediction type gradient lifting model.
In a second aspect of the embodiments of the present disclosure, a flue gas oxygen content load prediction apparatus based on sample migration is provided, and is applied to a joint learning framework, including:
the acquisition module is used for acquiring the equipment data of a first participant and the equipment data of a second participant under a joint learning architecture; the first participant is a participant who puts forward a forecast demand, and the second participant is other participants except the first participant;
a first training module for training a predictive classifier using the device data of the first participant and the device data of the second participant;
a calculation module to determine weight data of the device data of the first party with respect to the device data of the second party according to a predictive classifier;
the second training module is used for training the predictive gradient boost model based on the equipment data and the weight data of the second participant;
and the prediction module is used for predicting the oxygen content load of the smoke of the first participant equipment by utilizing the predicted gradient lifting model.
In a third aspect of the embodiments of the present disclosure, a computer 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: acquiring device data of a first participant and device data of a second participant under a joint learning architecture; the first participant is a participant who puts forward a forecast demand, and the second participant is other participants except the first participant; training a predictive classifier using the device data of the first participant and the device data of the second participant; determining, from the predictive classifier, weight data for the device data of the first participant in relation to the device data of the second participant; training a predictive gradient boost model based on the device data and the weight data of the second participant; and predicting the oxygen content load of the smoke of the first participant device by using a prediction type gradient lifting model. The method solves the problem that the prediction of the oxygen content load of the flue gas is inaccurate due to the data distribution difference of the energy equipment generated under different processes in the prior art, and saves the cost of the energy equipment sensor.
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 flowchart of a method for predicting flue gas oxygen content load based on sample migration according to an embodiment of the present disclosure;
FIG. 3 is a block diagram of a flue gas oxygen content load prediction device based on sample migration according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram of a computer device provided by 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 method and an apparatus for predicting flue gas oxygen content based on sample migration according to an embodiment of the present disclosure in detail with reference to the accompanying drawings.
Fig. 1 is a scene schematic diagram of an application scenario of an embodiment of the present disclosure. An application scenario of the embodiment of the present disclosure is a joint learning scenario, and fig. 1 is a schematic diagram of 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. Where a participant may be one client or a combination of multiple clients.
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 flowchart of a method for predicting an oxygen content load of flue gas based on sample migration according to an embodiment of the present disclosure. The flue gas oxygen content load prediction method based on sample migration of fig. 2 may be performed by the server of fig. 1. As shown in fig. 2, the method for predicting the load of oxygen content in flue gas based on sample migration includes:
s201, acquiring device data of a first participant and device data of a second participant under a joint learning architecture.
The first participant is a participant who puts forward the forecast demand, and the second participant is other participants except the first participant.
In particular, the first participant may be identified by receiving a device data set from the first participant with a device data set of the second participant; then, according to preset screening characteristics, screening the equipment data set of the first participant and the equipment data set of the second participant to respectively obtain the equipment data sample size of the first participant and the equipment data size of the second participant; and determining the device data sample size of the first participant and the device data size of the second participant as the device data of the first participant and the device data of the second participant respectively.
S202, training a prediction classifier by using the equipment data of the first participant and the equipment data of the second participant.
Specifically, the tag data of the device data of the first party and the tag data of the device data of the second party can be obtained by tagging the device data of the first party and the device data of the second party; further merging the tag data of the device data of the first party and the tag data of the device data of the second party to obtain merged tag data; and finally, training a prediction classifier according to the combined label data.
S203, determining weight data of the device data of the first participant with respect to the device data of the second participant according to the predictive classifier.
Specifically, a device fault probability value corresponding to the device data of the first participant and a device fault probability value corresponding to the device data of the second participant can be obtained by using the prediction classifier; the weight data of the device data of the first participant with respect to the device data of the second participant may then be determined based on the device failure probability value corresponding to the device data of the first participant and the device failure probability value corresponding to the device data of the second participant.
Further, for the purpose of respectively obtaining the device fault probability value corresponding to the device data of the first participant and the device fault probability value corresponding to the device data of the second participant by using the prediction classifier, the device data of the first participant and the device data of the second participant can be respectively classified by using the prediction classifier to obtain the device fault data corresponding to the device data of the first participant and the device fault data corresponding to the device data of the second participant; then, device fault probability values corresponding to the device fault data of the first participant and the device fault data of the second participant are calculated respectively.
And S204, training a predictive gradient lifting model based on the equipment data of the second participant and the weight data.
Specifically, the predictive gradient boost model may be trained based on device data of the second participant and weight data of the device data of the first participant with respect to the device data of the second participant; then, training a predictive gradient lifting model by using the obtained test data of the first participant to obtain an equipment prediction value of the first participant; obtaining a fitness value of the predictive gradient lifting model according to the norm of the error matrix of the equipment predicted value and the equipment expected value of the first participant; and finally, updating the particles in the population in the predictive gradient lifting model according to the fitness value of the predictive gradient lifting model to obtain the optimized predictive gradient lifting model.
Further, the optimization of the predictive gradient boost model can be realized by the following steps:
firstly, determining a population and particles in the population in a predictive gradient lifting model;
then, judging whether the fitness value corresponding to the particles in the current population is larger than the previous fitness value of the old particles; and if the average value of the population and the particles in the population in the predictive gradient lifting model is smaller than the preset value, updating the population and the particles in the population in the predictive gradient lifting model.
S205, predicting the oxygen content load of the smoke of the first party equipment by using a prediction type gradient lifting model.
According to the technical scheme provided by the embodiment of the disclosure, the device data of a first participant and the device data of a second participant under a joint learning architecture are obtained; the first participant is a participant who puts forward a forecast demand, and the second participant is other participants except the first participant; training a predictive classifier using the device data of the first participant and the device data of the second participant; determining, from the predictive classifier, weight data for the device data of the first participant in relation to the device data of the second participant; training a predictive gradient boost model based on the device data and the weight data of the second participant; and predicting the oxygen content load of the smoke of the first participant device by using a prediction type gradient lifting model. The method solves the problem of inaccuracy of flue gas oxygen content load prediction caused by data distribution difference of energy equipment generated under different processes in the prior art, and saves the cost of an energy equipment sensor.
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 flue gas oxygen content load prediction device based on sample migration according to an embodiment of the present disclosure. As shown in fig. 3, the apparatus for predicting flue gas oxygen content load based on sample migration, applied to a joint learning framework, includes:
an obtaining module 301, configured to obtain device data of a first participant and device data of a second participant in a joint learning architecture; the first participant is a participant who puts forward a forecast demand, and the second participant is other participants except the first participant;
a first training module 302 to train a predictive classifier using the device data of the first participant and the device data of the second participant;
a calculation module 303 for determining weight data of the device data of the first party with respect to the device data of the second party according to the predictive classifier;
a second training module 304, configured to train the predictive gradient boost model based on the device data and the weight data of the second participant;
and the prediction module 305 is configured to predict the flue gas oxygen content load of the first participant device by using the predicted gradient boost model.
According to the technical scheme provided by the embodiment of the disclosure, the device data of a first participant and the device data of a second participant under a joint learning architecture are obtained; the first participant is a participant who puts forward a forecast demand, and the second participant is other participants except the first participant; training a predictive classifier using the device data of the first participant and the device data of the second participant; determining, from the predictive classifier, weight data for the device data of the first participant in relation to the device data of the second participant; training a predictive gradient boost model based on the device data and the weight data of the second participant; and predicting the oxygen content load of the smoke of the first participant device by using a prediction type gradient lifting model. The method solves the problem that the prediction of the oxygen content load of the flue gas is inaccurate due to the data distribution difference of the energy equipment generated under different processes in the prior art, and saves the cost of the energy equipment sensor.
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 a computer device 4 provided by the disclosed embodiment. As shown in fig. 4, the computer device 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 computer device 4.
The computer device 4 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computer devices. Computer 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 a computer device 4 and is not intended to limit computer device 4 and may include more or fewer components than those shown, or some of the components may be combined, or different components, e.g., the computer 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 computer device 4, for example, a hard disk or a memory of the computer device 4. The memory 402 may also be an external storage device of the computer device 4, such as a plug-in hard disk provided on the computer device 4, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, memory 402 may also include both internal storage units of computer device 4 and external storage devices. The memory 402 is used for storing computer programs and other programs and data required by the computer 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/computer device and method may be implemented in other ways. For example, the above-described apparatus/computer device embodiments are merely illustrative, and for example, a division of modules or units, a division of logical functions only, an additional division may be made in actual implementation, multiple units or components may be combined or integrated with 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 flue gas oxygen content load prediction method based on sample migration is characterized in that the method is applied to a joint learning framework and comprises the following steps:
acquiring device data of a first participant and device data of a second participant under a joint learning architecture; the first participant is a participant who puts forward a forecast demand, and the second participant is other participants except the first participant;
training a predictive classifier using the device data of the first participant and the device data of the second participant;
determining, in accordance with the predictive classifier, weight data for the device data of the first participant in relation to the device data of the second participant;
training a predictive gradient boost model based on the device data and the weight data of the second participant;
and predicting the flue gas oxygen content load of the first participant device by using the predictive gradient lifting model.
2. The method of claim 1, wherein obtaining device data of a first participant and device data of a second participant in a joint learning architecture comprises:
receiving a device data set from a first participant with a device data set of a second participant;
screening the equipment data set of the first participant and the equipment data set of the second participant according to preset screening characteristics so as to respectively obtain the equipment data sample size of the first participant and the equipment data size of the second participant;
and respectively determining the device data sample size of the first participant and the device data size of the second participant as the device data of the first participant and the device data of the second participant.
3. The method of claim 1, wherein using the device data of the first participant and the device data of the second participant to train a predictive classifier comprises:
labeling the device data of the first party and the device data of the second party to obtain label data of the device data of the first party and label data of the device data of the second party;
merging the tag data of the device data of the first party and the tag data of the device data of the second party to obtain merged tag data;
and training a prediction classifier according to the combined label data.
4. The method of claim 1, wherein determining weight data for the device data of the first participant with respect to the device data of the second participant according to the predictive classifier comprises:
respectively obtaining an equipment fault probability value corresponding to the equipment data of the first participant and an equipment fault probability value corresponding to the equipment data of the second participant by using the prediction classifier;
and determining the weight data of the device data of the first participant relative to the device data of the second participant according to the device fault probability value corresponding to the device data of the first participant and the device fault probability value corresponding to the device data of the second participant.
5. The method of claim 4, wherein obtaining, using the predictive classifier, a device failure probability value corresponding to the device data of the first participant and a device failure probability value corresponding to the device data of the second participant respectively comprises:
classifying the equipment data of the first party and the equipment data of the second party by using the prediction classifier respectively to obtain equipment fault data corresponding to the equipment data of the first party and equipment fault data corresponding to the equipment data of the second party;
and respectively calculating the device fault probability value corresponding to the device fault data of the first participant and the device fault probability value corresponding to the device fault data of the second participant.
6. The method of claim 1, wherein training the predictive gradient boost model based on the device data and the weight data of the second participant further comprises:
training a predictive gradient boost model based on the device data and the weight data of the second participant;
acquiring test data of a first participant to train a predictive gradient lifting model so as to obtain an equipment prediction value of the first participant;
obtaining a fitness value of a predictive gradient lifting model according to a norm of an error matrix of a predicted value and an expected value of equipment of a first participant;
and updating the particles in the population in the predictive gradient lifting model according to the fitness value to obtain an optimized predictive gradient lifting model.
7. The method of claim 6, wherein updating the population of particles in the predictive gradient boost model according to the fitness value to obtain the optimized predictive gradient boost model comprises:
determining a population in the predictive gradient boost model and particles in the population;
judging whether the fitness value corresponding to the particles in the current population is larger than the fitness value of the old particles;
and if the number of the particles in the population is less than the preset number, updating the population in the predictive gradient boost model and the particles in the population.
8. A flue gas oxygen content load prediction device based on sample migration is characterized in that the device applied to a joint learning framework comprises the following components:
the acquisition module is used for acquiring the equipment data of a first participant and the equipment data of a second participant under a joint learning architecture; the first participant is a participant who puts forward a forecast demand, and the second participant is other participants except the first participant;
a first training module trains a predictive classifier using the device data of the first participant and the device data of the second participant;
a calculation module to determine weight data of the device data of the first party with respect to the device data of the second party according to the predictive classifier;
the second training module is used for training the predictive gradient boost model based on the equipment data of the second participant and the weight data set;
and the prediction module is used for predicting the smoke oxygen content load of the first participant equipment by using the prediction type gradient lifting model.
9. A computer 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 one 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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