CN114577481B - Pollution index monitoring method and device for gas internal combustion engine - Google Patents
Pollution index monitoring method and device for gas internal combustion engine Download PDFInfo
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- CN114577481B CN114577481B CN202011403144.3A CN202011403144A CN114577481B CN 114577481 B CN114577481 B CN 114577481B CN 202011403144 A CN202011403144 A CN 202011403144A CN 114577481 B CN114577481 B CN 114577481B
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 264
- 238000002485 combustion reaction Methods 0.000 title claims abstract description 181
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000012549 training Methods 0.000 claims description 27
- 239000007789 gas Substances 0.000 description 156
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 12
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 11
- 229910002091 carbon monoxide Inorganic materials 0.000 description 7
- 238000010586 diagram Methods 0.000 description 4
- 239000003546 flue gas Substances 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 3
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000013145 classification model Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000012806 monitoring device Methods 0.000 description 2
- 239000001301 oxygen Substances 0.000 description 2
- 229910052760 oxygen Inorganic materials 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 239000008186 active pharmaceutical agent Substances 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M15/00—Testing of engines
- G01M15/04—Testing internal-combustion engines
- G01M15/10—Testing internal-combustion engines by monitoring exhaust gases or combustion flame
- G01M15/102—Testing internal-combustion engines by monitoring exhaust gases or combustion flame by monitoring exhaust gases
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M15/00—Testing of engines
- G01M15/04—Testing internal-combustion engines
- G01M15/05—Testing internal-combustion engines by combined monitoring of two or more different engine parameters
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
- Y02A50/20—Air quality improvement or preservation, e.g. vehicle emission control or emission reduction by using catalytic converters
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- Combustion & Propulsion (AREA)
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- Combined Controls Of Internal Combustion Engines (AREA)
Abstract
The invention discloses a pollution index monitoring method and device of a gas internal combustion engine, a computer readable storage medium and electronic equipment, wherein the method comprises the following steps: sending a pollution index monitoring model request of the gas internal combustion engine to a joint learning server; receiving a pollution index monitoring model sent by the joint learning server; and determining pollution index data of the gas internal combustion engine according to the pollution index monitoring model. According to the technical scheme, a sensor is not required to be installed, the pollution index monitoring model depends on real data, is not easily influenced by external environment, and can accurately determine pollution index data of the gas internal combustion engine.
Description
Technical Field
The invention relates to the technical field of energy, in particular to a pollution index monitoring method and device for a gas internal combustion engine.
Background
The concentration of carbon monoxide and/or nitrogen oxides in the flue gas emitted by a gas internal combustion engine is one of the important marks for measuring whether the gas internal combustion engine is operated in an environment-friendly way.
Currently, sensors are commonly used in the industry to measure the concentration of carbon monoxide and/or nitrogen oxides in the flue gas emitted by gas internal combustion engines.
However, the sensor is susceptible to environmental factors, resulting in large errors in the measured concentration of carbon monoxide and/or nitrogen oxides.
Disclosure of Invention
The invention provides a pollution index monitoring method, a device, a computer readable storage medium and electronic equipment of a gas internal combustion engine, wherein a sensor is not required to be installed, a pollution index monitoring model depends on real data, is not easily influenced by external environment, and can accurately determine pollution index data of the gas internal combustion engine.
In a first aspect, the present invention provides a pollution index monitoring method for a gas internal combustion engine, including:
sending a pollution index monitoring model request of the gas internal combustion engine to a joint learning server;
receiving a pollution index monitoring model sent by the joint learning server;
and determining pollution index data of the gas internal combustion engine according to the pollution index monitoring model.
In a second aspect, the present invention provides a pollution index monitoring method for a gas internal combustion engine, including:
receiving a pollution index monitoring model request of a gas internal combustion engine sent by an application client;
judging whether a model corresponding to the pollution index monitoring model request exists in a model database or not;
Determining a pollution index monitoring model according to a judging result of judging whether the model corresponding to the pollution index monitoring model request exists in the model database;
and sending the pollution index monitoring model to the application client so that the application client can determine the pollution index data of the gas internal combustion engine according to the pollution index monitoring model.
In a third aspect, the present invention provides an application client, including:
the first sending module is used for sending a pollution index monitoring model request of the gas internal combustion engine to the joint learning server;
the first receiving module is used for receiving the pollution index monitoring model sent by the joint learning server;
and the application module is used for determining the pollution index data of the gas internal combustion engine according to the pollution index monitoring model.
In a fourth aspect, the present invention provides a joint learning server, including:
the second receiving module is used for receiving a pollution index monitoring model request of the gas internal combustion engine, which is sent by the application client;
the judging module is used for judging whether a model corresponding to the pollution index monitoring model request exists in the model database;
the model determining module is used for determining a pollution index monitoring model according to a judging result of judging whether the model corresponding to the pollution index monitoring model request exists in the model database;
And the second sending module is used for sending the pollution index monitoring model to the application client so that the application client can determine the pollution index data of the gas internal combustion engine according to the pollution index monitoring model.
In a fifth aspect, the present invention provides a computer readable storage medium comprising execution instructions which, when executed by a processor of an electronic device, performs the method of any of the first or second aspects.
In a sixth aspect, the present invention provides an electronic device comprising a processor and a memory storing execution instructions, the processor performing the method according to any one of the first or second aspects when executing the execution instructions stored in the memory.
The invention provides a pollution index monitoring method, a device, a computer readable storage medium and electronic equipment of a gas internal combustion engine. In summary, according to the technical scheme provided by the invention, a sensor is not required to be installed, the pollution index monitoring model depends on real data, is not easily influenced by external environment, and can accurately determine pollution index data of the gas internal combustion engine.
Further effects of the above-described non-conventional preferred embodiments will be described below in connection with the detailed description.
Drawings
In order to more clearly illustrate the embodiments of the invention or the prior art solutions, the drawings which are used in the description of the embodiments or the prior art will be briefly described below, it being obvious that the drawings in the description below are only some of the embodiments described in the present invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flow chart of a pollution index monitoring method for a gas combustion engine according to an embodiment of the present invention;
FIG. 2 is a flowchart of another method for monitoring pollution index of a gas combustion engine according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an application client according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a joint learning server according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;
fig. 6 is a schematic view of a scenario provided in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the method for monitoring pollution indexes of a gas internal combustion engine according to the embodiment of the invention is provided. The method provided by the embodiment of the invention can be applied to an application client, and particularly can be applied to a server or a general computer. In this embodiment, the method specifically includes the following steps:
and step 101, sending a pollution index monitoring model request of the gas internal combustion engine to a joint learning server.
Specifically, an application client of the gas internal combustion engine sends a pollution index monitoring model request of the gas internal combustion engine through an API interface, namely, a pollution index monitoring model call is initiated. Optionally, the application client is provided on a local server. An application client is understood to be a client for meeting the business requirements of a gas combustion engine, for example, a client for predicting pollution index data, fault types of a gas combustion engine, and in the scenario of monitoring pollution indexes, the application client does not participate in joint learning, only as a party to a model application, in other words, the gas combustion engine does not participate in joint learning.
Specifically, the joint learning server is used for joint learning with a joint learning client (a client for joint learning) to obtain a joint learning model, and storing the joint learning model, and is also used for providing the model for an application client. And the method can be applied to the interaction scene of at least one application client and one joint learning server, or can also be applied to the interaction scene of at least one application client and a plurality of joint learning servers, namely, a model database to be called is arranged on each joint learning server, wherein any joint learning server can provide services for the application client, and the calling of the model in the model database is realized. It can also be appreciated that in the two scenarios described above, each joint learning server may be provided with a model database and a control layer. The model database stores at least one joint learning model, and the control layer can realize the calling of the model in the model database based on the request of the model calling of the application client.
Step 102, receiving a pollution index monitoring model sent by the joint learning server.
The pollution index monitoring model is used for predicting pollution index data of the gas internal combustion engine.
And step 103, determining pollution index data of the gas internal combustion engine according to the pollution index monitoring model.
In particular, the pollution index data comprises the concentration of nitrogen oxides and/or carbon monoxide.
In some possible implementations, the pollution index monitoring models are multiple, the multiple pollution index monitoring models are screened based on service information of the pollution index monitoring models, the target pollution index monitoring model is determined, and the pollution index data of the gas internal combustion engine is predicted based on the target pollution index monitoring model and the operation data of the gas internal combustion engine. It should be understood that the service information indicates the application of the model, and the reference value of the predicted pollution index data of the gas internal combustion engine is ensured based on the actual application of the model. Specifically, step 103 includes:
and receiving service information of the pollution index monitoring model sent by the joint learning server.
Determining a target pollution index monitoring model from the pollution index monitoring models according to the service information of the pollution index monitoring models;
And determining pollution index data of the gas internal combustion engine according to the target pollution index monitoring model and the operation data of the gas internal combustion engine.
It is understood that the service information refers to the relevant information generated by applying the pollution index monitoring model and the relevant information of the pollution index monitoring model itself. Specifically, the service information includes the number of model downloads, the model download users, the source of model training data, the amount of model training data, the model accuracy, the model generation time, and the number of model updates. The model accuracy is determined based on feedback data uploaded by an application client using the model, wherein the feedback data comprises a predicted value and an actual value. It should be noted that, the above service information is merely an example, in practical application, the service information may be various, and specifically, a field name corresponding to the service information that is desired to be obtained may be preset according to an actual service requirement, and the field name corresponding to the service information is encapsulated in the request, so that the request carries the field name corresponding to the service information. The operation data of the gas internal combustion engine is data generated in the operation process of the gas internal combustion engine, including but not limited to on-off state, gas flow, gas temperature, gas pressure, air temperature, air flow, water supply temperature, main steam pressure, main steam temperature and main steam flow, and is specifically determined according to practical situations.
As a possible case, based on the service information of the pollution index monitoring model, the plurality of pollution index monitoring models are automatically screened, the target pollution index monitoring model is determined, and the user experience is improved. Specifically, the target pollution index monitoring model may be determined by:
and acquiring the model requirements of the gas internal combustion engine, and selecting each pollution index monitoring model according to the model requirements and the service information of each pollution index monitoring model so as to determine a target pollution index monitoring model.
The model requirement refers to a condition that the model should meet, and includes a plurality of model conditions, for example, the model accuracy requirement, the model generation time requirement, the model downloading frequency requirement, the model training data volume requirement and the like, and the target pollution index monitoring model is a joint learning model meeting the model requirement. In a first example, if only one pollution index monitoring model meets the model requirement, determining the pollution index monitoring model as a target pollution index monitoring model; in a second example, if two or more pollution index monitoring models meet the model requirements, determining the pollution index monitoring models as target pollution index monitoring models respectively; in a third example, setting respective priorities of different model conditions in the model requirements, determining model conditions met by the pollution index monitoring model for each pollution index monitoring model, and determining scores of the pollution index monitoring models according to the priorities of the model conditions met by the pollution index monitoring model; then, the pollution index monitoring model with the largest score is determined as the target pollution index monitoring model.
As another possible case, the service information of the pollution index monitoring model is displayed to interact with the user, and the user selects the target pollution index monitoring model to determine the target pollution index monitoring model so as to meet the user requirements. Specifically, the target pollution index monitoring model may be determined by:
displaying respective icons of the pollution index monitoring models and respective business information of the pollution index monitoring models, and determining a target pollution index monitoring model from the pollution index monitoring models according to the acquired external trigger information.
Specifically, the application client corresponding to the gas internal combustion engine displays respective icons and respective business information of all pollution index monitoring models, so that a user knows the application condition of the models and the condition of the models, then clicks the pollution index monitoring models to download, determines the downloaded pollution index monitoring models as target pollution index monitoring models, interacts with the user, and ensures the reference value of the target pollution index monitoring models.
In the above embodiment, when there are a plurality of target pollution index monitoring models, the pollution index data of the gas internal combustion engine predicted by each target pollution index monitoring model is averaged to obtain the pollution index data of the gas internal combustion engine.
Referring to fig. 6, assuming that the carbon monoxide concentration of the gas internal combustion engine C1 in the energy station C is to be predicted, the monitoring client Cc in the local operation and maintenance server sends a pollution index monitoring model request, then the monitoring client Cc receives the pollution index monitoring model sent by the joint learning server S, predicts the carbon monoxide concentration of the gas internal combustion engine C1 according to the pollution index monitoring model, and uploads the prediction result to the joint learning server S, and if a true value exists, uploads the true value to the joint learning server S.
According to the technical scheme, the beneficial effects of the embodiment are as follows: the pollution index monitoring model is called from the joint learning server through the pollution index monitoring model request, so that the pollution index monitoring model is rapidly determined, and the efficiency of model acquisition is improved; and selecting a model through service information of a pollution index monitoring model, and ensuring the predicted reference value of the oxygen content of the flue gas.
As shown in fig. 2, another method for monitoring pollution indexes of a gas internal combustion engine according to an embodiment of the present invention is provided. The method provided by the embodiment of the invention can be applied to a joint learning server, and can be particularly applied to a server or a general computer. In this embodiment, the method specifically includes the following steps:
Step 201, receiving a pollution index monitoring model request of a gas internal combustion engine sent by an application client.
The description of step 101 is not repeated here.
Step 202, judging whether a model corresponding to the pollution index monitoring model request exists in a model database.
Before model call is performed, whether a model corresponding to a pollution index monitoring model request exists in a model database needs to be determined, and only the model call can be realized.
In some possible implementations, the model database includes models to be called and respective model information of each model to be called; specifically, step 202 includes:
and judging whether the models to be called are matched with the models corresponding to the pollution index monitoring model request according to the model information of the models to be called aiming at each model to be called.
The model database is used for storing the model to be called and the model information of the model to be called, and specifically, the model to be called is a joint learning model. The model information is used to describe information of the model to be called, and may be, for example, information of model use, model name, number of model downloads, model download user, model training data source, model training data amount, model precision, model generation time, number of model updates, and the like.
Specifically, according to the model information of the model to be called, judging whether the model to be called is a model corresponding to the pollution index monitoring model request, and in practical application, when the pollution index monitoring model name exists in the model information, or words with the same meaning as the pollution index monitoring model name but different expressions are expressed, describing that the model to be called corresponding to the model information is the model corresponding to the pollution index monitoring model request. At the moment, extracting a model corresponding to the pollution index monitoring model request from a model database, and completing model calling.
And 203, determining a pollution index monitoring model according to a judging result of judging whether the model corresponding to the pollution index monitoring model request exists in the model database.
In some possible implementations, the determination result includes a model corresponding to the pollution index monitoring model request existing in the model database; step 103 comprises:
and determining a model corresponding to the pollution index monitoring model request from the model database, and determining the model corresponding to the pollution index monitoring model request as a pollution index monitoring model.
In some possible implementations, when the model corresponding to the pollution index monitoring model request exists in the model database, the method further includes:
Determining service information of the pollution index monitoring model from the model database;
and sending the service information of the pollution index monitoring model to the application client.
The respective service information of the pollution index monitoring model is sent to the application client, so that a user can know the condition of the model, and the user can conveniently select the model, thereby ensuring the accuracy of predicted pollution index data.
In some possible implementations, the determination result includes that the model corresponding to the pollution index monitoring model request does not exist in the model database; step 103 comprises:
selecting a combined gas internal combustion engine, wherein the combined gas internal combustion engine passes through a combined learning invitation, and the similarity between the combined gas internal combustion engine and the gas internal combustion engine is not smaller than a preset threshold;
performing joint learning according to respective model training data sets of the joint gas internal combustion engines to obtain respective local pollution index monitoring models;
determining a combined pollution index monitoring model according to each local pollution index monitoring model;
and determining the combined pollution index monitoring model as a pollution index monitoring model.
Each energy station is used as a node in the Internet of things, a client is arranged, if data in the energy station is used for joint learning, the energy station is called a joint energy station, a gas internal combustion engine in the energy station is called a joint gas internal combustion engine, and a client corresponding to the energy station is called a joint learning client. The joint learning ensures that the private data of the user is protected to the greatest extent through the distributed training and encryption technology, so that the trust of the user on the artificial intelligence technology is improved. In the embodiment of the application, under the joint learning mechanism, each participant (the joint learning client corresponding to each joint gas internal combustion engine) contributes the encrypted data model to the union and jointly trains a joint model. And uploading the gas internal combustion engine operation data of the combined gas internal combustion engine to a corresponding combined learning client, and storing the gas internal combustion engine operation data by the combined learning client.
In this embodiment, when there is no model corresponding to the pollution index monitoring model request in the model database, a model needs to be built at this time. Determining a combined gas internal combustion engine similar to the gas internal combustion engine in the energy station; and then, carrying out joint learning based on respective model training data sets of the joint gas internal combustion engines to obtain a local pollution index monitoring model, wherein the model training data sets comprise gas internal combustion engine operation data of the joint gas internal combustion engines, and specifically, the gas internal combustion engine operation data comprise on-off state, gas flow, gas temperature, gas pressure, air temperature, air flow, water supply temperature, main steam pressure, main steam temperature, main steam flow and historical flue gas oxygen content (label). And then, determining a combined pollution index monitoring model based on each local pollution index monitoring model, and determining the combined pollution index monitoring model as a pollution index monitoring model. The local pollution index monitoring model is a model obtained by model training based on a model training data set of the combined gas internal combustion engine, or a model obtained by model training based on model training data sets of a plurality of combined gas internal combustion engines in a combined learning client.
The combined gas internal combustion engine can be determined specifically by the following 2 implementation modes:
in the embodiment 1, the joint gas internal combustion engine is determined based on the description information of the candidate gas internal combustion engines that can participate in the joint learning and the description information of the gas internal combustion engines, and the joint gas internal combustion engine and the gas internal combustion engine are similar. Specifically, the combined gas internal combustion engine may be determined by the following implementation:
acquiring description information of the gas internal combustion engines carried by the pollution index monitoring model request and description information of each candidate gas internal combustion engine;
for each candidate gas internal combustion engine, determining the similarity between the gas internal combustion engine and the candidate gas internal combustion engine according to the description information of the gas internal combustion engine and the description information of the candidate gas internal combustion engine;
and determining the combined gas internal combustion engine according to the similarity between each candidate gas internal combustion engine and the gas internal combustion engine.
The pollution index monitoring model requests to carry description information of the gas internal combustion engine, then obtains the description information of the candidate gas internal combustion engine capable of carrying out joint learning, calculates the similarity between the description information of the gas internal combustion engine and the description information of the candidate gas internal combustion engine to determine the similarity between the candidate gas internal combustion engine and the gas internal combustion engine, and determines the candidate gas internal combustion engine corresponding to the similarity which is not smaller than a preset threshold value as the joint gas internal combustion engine. Wherein the descriptive information includes a plurality of parameters and parameter values for each parameter. The above parameters include, but are not limited to, rated capacity, rated efficiency, operating mode, model (indicating the performance, specification and size of the gas internal combustion engine), brand, and job site, as specifically needed to be determined in conjunction with the actual situation. It can be appreciated that the higher the similarity between the description information of the gas internal combustion engine and the description information of the combined gas internal combustion engine, the higher the reference value of the combined gas internal combustion engine, so as to ensure the accuracy of the combined gas internal combustion engine predicted subsequently. Preferably, the gas combustion engine and the combined gas combustion engine should be identical in model. Optionally, the similarity of each parameter in the description information of the gas internal combustion engine and the candidate gas internal combustion engine is determined based on the parameter value of each parameter in the description information of the gas internal combustion engine and the candidate gas internal combustion engine, weighted average is performed on the similarity of each parameter, and the result is determined as the similarity between the gas internal combustion engine and the candidate gas internal combustion engine. In practical application, the description information of the gas internal combustion engine can be taken as a model input, the combined gas internal combustion engine is taken as a model output, a classification model is trained, and the description information of the candidate gas internal combustion engine is input into the trained classification model, so that whether the corresponding energy station is the combined gas internal combustion engine is determined.
Implementation 2, based on the joint learning invitation, determines a joint gas internal combustion engine. Specifically, the combined gas internal combustion engine may be determined by the following implementation:
determining a reference combined gas internal combustion engine according to the similarity between each candidate gas internal combustion engine and the gas internal combustion engine;
respectively sending joint learning invitations to the joint learning clients corresponding to the reference joint energy stations;
each reference joint gas internal combustion engine agreeing to the joint learning invitation is determined as a joint gas internal combustion engine, respectively.
Here, the candidate gas internal combustion engines with the similarity not smaller than the preset threshold value are determined as reference joint gas internal combustion engines, joint learning invitations are respectively sent to the joint learning clients corresponding to the reference joint gas internal combustion engines respectively, and if the joint learning clients feed back the invitations to pass, the corresponding reference joint gas internal combustion engines are determined as the joint gas internal combustion engines.
The joint model can be constructed specifically by the following 2 implementation modes:
in the implementation mode 1, aiming at each joint learning client, when the difference of a plurality of gas internal combustion engines in a joint energy station corresponding to the joint learning client is large, the joint learning client performs joint learning based on respective model training data sets of a plurality of joint gas internal combustion engines stored in the joint learning client to obtain a local pollution index monitoring model, and when a plurality of gas internal combustion engines in the joint energy station corresponding to the joint learning client are similar, the joint learning client performs joint learning based on respective model training data sets of all the gas internal combustion engines stored in the joint learning client to obtain a local pollution index monitoring model; the local pollution index monitoring models corresponding to the combined learning clients are obtained, the combined pollution index monitoring model is built based on the local pollution index monitoring models corresponding to the combined learning clients, and compared with one local model corresponding to each combined gas internal combustion engine, the calculation amount can be reduced, and the calculation efficiency is improved.
In the implementation mode 2, for each combined gas internal combustion engine, the local pollution index monitoring model is obtained by performing combined learning based on the model training data set of the combined gas internal combustion engine, the local pollution index monitoring model corresponding to each of the plurality of combined gas internal combustion engines is obtained, and the combined model is built based on the local pollution index monitoring model corresponding to each of the plurality of combined gas internal combustion engines.
And step 204, sending the pollution index monitoring model to the application client so that the application client can determine the pollution index data of the gas internal combustion engine according to the pollution index monitoring model.
The pollution index monitoring model may be understood as a model which is input as operation data of the gas internal combustion engine and output as pollution index data, for example, may be a neural network, a support vector machine, a lifting decision tree, etc., and it should be understood that the present embodiment is not intended to limit the internal structure of the pollution index monitoring model, and any model which can take operation data of the gas internal combustion engine as input and pollution index data as output is applicable to the present embodiment.
Referring to fig. 6, assuming that the carbon monoxide concentration of the gas internal combustion engine C1 in the energy station C is to be predicted, the joint learning server S receives the pollution index monitoring model request from the monitoring client Cc, when the model database in the joint learning server S has no model corresponding to the pollution index monitoring model request, if the joint gas internal combustion engines are A1, A3, B1, B2, the joint learning client Ac performs model training based on the operation data of the gas internal combustion engines A1, A3 in the joint energy station a, and uploads the model parameters to the joint learning server S, the joint learning client Bc performs model training based on the operation data of the gas internal combustion engines B1, B2 in the joint energy station B, and uploads the model parameters to the joint learning server S, the joint learning server S issues the aggregated model to the joint learning client Ac and the joint learning client Bc for model iteration, and the joint learning client Ac and the joint learning client Bc respectively obtain local pollution index monitoring models, and the joint pollution index monitoring models obtained by the joint learning client and the joint learning client Bc are fused. Here, the joint learning client Ac is disposed on the local operation server As, the joint learning client Bc is disposed on the local operation server Bs, and the joint learning client Cc is disposed on the local operation server Cs.
According to the technical scheme, the beneficial effects of the embodiment are as follows: different model acquisition strategies are determined through the relation between the pollution index monitoring model request and the model database, the pollution index monitoring model is acquired based on the model acquisition strategies, a sensor is not required to be installed, the obtained pollution index monitoring model is not easily influenced by the external environment, and the pollution index data of the gas internal combustion engine can be accurately measured.
Based on the same concept as the method embodiment of the present invention, please refer to fig. 3, the embodiment of the present invention further provides an application client, which includes:
a first sending module 301, configured to send a pollution index monitoring model request of the gas internal combustion engine to a joint learning server;
the first receiving module 302 is configured to receive the pollution index monitoring model sent by the joint learning server;
and the application module 303 is used for determining the pollution index data of the gas internal combustion engine according to the pollution index monitoring model.
In one embodiment of the present invention, the application module 303 includes: the device comprises a receiving unit, a selecting unit and an application unit; wherein,
the receiving unit is used for receiving the service information of the pollution index monitoring model sent by the joint learning server;
The selection unit is used for determining a target pollution index monitoring model from the pollution index monitoring models according to the service information of the pollution index monitoring models;
the application unit is used for determining pollution index data of the gas internal combustion engine according to the target pollution index monitoring model and the operation data of the gas internal combustion engine.
In one embodiment of the present invention, the selecting unit is configured to obtain a model requirement of the gas internal combustion engine, and select each pollution index monitoring model according to the model requirement and respective service information of each pollution index monitoring model, so as to determine a target pollution index monitoring model.
In one embodiment of the present invention, the selecting unit is configured to display respective icons of the pollution index monitoring models and respective service information of the pollution index monitoring models, and determine a target pollution index monitoring model from the pollution index monitoring models according to the acquired external trigger information.
Based on the same concept as the method embodiment of the present invention, please refer to fig. 4, the embodiment of the present invention further provides a joint learning server, which includes:
The second receiving module 401 is configured to receive a pollution index monitoring model request of the gas internal combustion engine sent by the application client;
a judging module 402, configured to judge whether a model corresponding to the pollution index monitoring model request exists in a model database;
a model determining module 403, configured to determine a pollution index monitoring model according to a determination result of determining whether the model corresponding to the pollution index monitoring model request exists in the model database;
and the second sending module 404 is configured to send the pollution index monitoring model to the application client, so that the application client determines pollution index data of the gas internal combustion engine according to the pollution index monitoring model.
In one embodiment of the present invention, the model database includes models to be called and respective model information of each of the models to be called;
the judging module 402 is configured to judge, for each model to be invoked, whether the models to be invoked and the model corresponding to the pollution index monitoring model request are matched according to the model information of the model to be invoked.
In one embodiment of the present invention, the determination result includes a model corresponding to the pollution index monitoring model request existing in the model database;
The model determining module 403 is configured to determine, from the model database, a model corresponding to the pollution index monitoring model request, and determine, as a pollution index monitoring model, the model corresponding to the pollution index monitoring model request.
In one embodiment of the present invention, further comprising: an information determining module and a third transmitting module; wherein,
the information determining module is used for determining service information of the pollution index monitoring model from the model database;
and the third sending module is used for sending the service information of the pollution index monitoring model to the application client.
In one embodiment of the present invention, the determination result includes a model corresponding to the pollution index monitoring model request not existing in the model database;
the model determination module 403 includes: the training device comprises a selection unit, a training unit and a model determination unit; wherein,
the selection unit is used for selecting a combined gas internal combustion engine, the combined gas internal combustion engine passes through the combined learning invitation, and the similarity between the combined gas internal combustion engine and the gas internal combustion engine is not smaller than a preset threshold;
the training unit is used for carrying out joint learning according to respective model training data sets of the joint gas internal combustion engines to obtain each local pollution index monitoring model;
The model determining unit is used for determining a combined pollution index monitoring model according to each local pollution index monitoring model; and determining the combined pollution index monitoring model as a pollution index monitoring model.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. At the hardware level, the electronic device comprises a processor 501 and a memory 502 storing executable instructions, optionally together with an internal bus 503 and a network interface 504. The Memory 502 may include a Memory 5021, such as a Random-Access Memory (RAM), and may also include a non-volatile Memory 5022 (non-volatile Memory), such as at least 1 disk storage; the processor 501, the network interface 504, and the memory 502 may be interconnected by an internal bus 503, which internal bus 503 may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or an EISA (Extended Industry Standard Architecture ) bus, etc.; the internal bus 503 may be divided into an address bus, a data bus, a control bus, etc., and is represented by only one double-headed arrow in fig. 5 for convenience of illustration, but does not represent only one bus or one type of bus. Of course, the electronic device may also include hardware required for other services. When the processor 501 executes the execution instructions stored in the memory 502, the processor 501 performs the method of any one of the embodiments of the present invention and is at least used to perform the method as shown in fig. 1 or fig. 2.
In one possible implementation, the processor reads the corresponding execution instruction from the nonvolatile memory to the memory and then runs the execution instruction, and may also acquire the corresponding execution instruction from other devices to form a pollution index monitoring device of the gas internal combustion engine on a logic level. The processor executes the execution instructions stored in the memory to realize the pollution index monitoring method of the gas internal combustion engine provided in any embodiment of the invention through the executed execution instructions.
The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The embodiment of the invention also provides a computer readable storage medium, which comprises execution instructions, when the processor of the electronic device executes the execution instructions, the processor executes the method provided in any embodiment of the invention. The electronic device may specifically be an electronic device as shown in fig. 5; the execution instruction is a computer program corresponding to a pollution index monitoring device of a gas internal combustion engine.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method or a computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware aspects.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts of the embodiments are all referred to each other, and each embodiment is mainly described in the differences from the other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The foregoing is merely exemplary of the present invention and is not intended to limit the present invention. Various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are to be included in the scope of the claims of the present invention.
Claims (6)
1. A pollution index monitoring method for a gas internal combustion engine, comprising:
sending a pollution index monitoring model request of the gas internal combustion engine to a joint learning server;
receiving a pollution index monitoring model sent by the joint learning server;
determining pollution index data of the gas internal combustion engine according to the pollution index monitoring model, including:
receiving service information of the pollution index monitoring model sent by the joint learning server;
determining a target pollution index monitoring model from the pollution index monitoring models according to the service information of the pollution index monitoring models;
determining pollution index data of the gas internal combustion engine according to the target pollution index monitoring model and the operation data of the gas internal combustion engine;
determining a target pollution index monitoring model from the pollution index monitoring models according to the respective service information of the pollution index monitoring models, wherein the target pollution index monitoring model comprises the following components:
Acquiring the model requirements of the gas internal combustion engine, and selecting each pollution index monitoring model according to the model requirements and the respective service information of each pollution index monitoring model so as to determine a target pollution index monitoring model;
or,
displaying respective icons of the pollution index monitoring models and respective business information of the pollution index monitoring models, and determining a target pollution index monitoring model from the pollution index monitoring models according to the acquired external trigger information.
2. A pollution index monitoring method for a gas internal combustion engine, comprising:
receiving a pollution index monitoring model request of a gas internal combustion engine sent by an application client;
judging whether a model corresponding to the pollution index monitoring model request exists in a model database or not;
determining a pollution index monitoring model according to a judging result of judging whether the model corresponding to the pollution index monitoring model request exists in the model database;
transmitting the pollution index monitoring model to the application client so that the application client can determine pollution index data of the gas internal combustion engine according to the pollution index monitoring model;
The judging result comprises a model corresponding to the pollution index monitoring model request in the model database; determining the pollution index monitoring model according to the judging result of judging whether the model corresponding to the pollution index monitoring model request exists in the model database, comprising:
determining a model corresponding to the pollution index monitoring model request from the model database, and determining the model corresponding to the pollution index monitoring model request as a pollution index monitoring model;
or,
the judging result comprises a model corresponding to the pollution index monitoring model request which does not exist in the model database; determining the pollution index monitoring model according to the judging result of judging whether the model corresponding to the pollution index monitoring model request exists in the model database, comprising:
selecting a combined gas internal combustion engine, wherein the combined gas internal combustion engine passes through a combined learning invitation, and the similarity between the combined gas internal combustion engine and the gas internal combustion engine is not smaller than a preset threshold;
performing joint learning according to respective model training data sets of the joint gas internal combustion engines to obtain respective local pollution index monitoring models;
And determining a combined pollution index monitoring model according to each local pollution index monitoring model, and determining the combined pollution index monitoring model as a pollution index monitoring model.
3. The method according to claim 2, wherein the model database comprises models to be invoked and respective model information of each of the models to be invoked;
judging whether a model corresponding to the pollution index monitoring model request exists in a model database or not, and comprising the following steps:
and judging whether the models to be called are matched with the models corresponding to the pollution index monitoring model request according to the model information of the models to be called aiming at each model to be called.
4. The method as recited in claim 2, further comprising:
determining service information of the pollution index monitoring model from the model database;
and sending the service information of the pollution index monitoring model to the application client.
5. An application client, comprising:
the first sending module is used for sending a pollution index monitoring model request of the gas internal combustion engine to the joint learning server;
the first receiving module is used for receiving the pollution index monitoring model sent by the joint learning server;
The application module is used for determining pollution index data of the gas internal combustion engine according to the pollution index monitoring model;
the application module comprises: the device comprises a receiving unit, a selecting unit and an application unit; wherein,
the receiving unit is used for receiving the service information of the pollution index monitoring model sent by the joint learning server;
the selection unit is used for determining a target pollution index monitoring model from the pollution index monitoring models according to the service information of the pollution index monitoring models;
the application unit is used for determining pollution index data of the gas internal combustion engine according to the target pollution index monitoring model and the operation data of the gas internal combustion engine;
the selection unit is specifically configured to:
acquiring the model requirements of the gas internal combustion engine, and selecting each pollution index monitoring model according to the model requirements and the respective service information of each pollution index monitoring model so as to determine a target pollution index monitoring model;
or,
displaying respective icons of the pollution index monitoring models and respective business information of the pollution index monitoring models, and determining a target pollution index monitoring model from the pollution index monitoring models according to the acquired external trigger information.
6. A joint learning server, comprising:
the second receiving module is used for receiving a pollution index monitoring model request of the gas internal combustion engine, which is sent by the application client;
the judging module is used for judging whether a model corresponding to the pollution index monitoring model request exists in the model database;
the model determining module is used for determining a pollution index monitoring model according to a judging result of judging whether the model corresponding to the pollution index monitoring model request exists in the model database;
the second sending module is used for sending the pollution index monitoring model to the application client so that the application client can determine pollution index data of the gas internal combustion engine according to the pollution index monitoring model;
the judging result comprises a model corresponding to the pollution index monitoring model request in the model database; the model determining module is specifically configured to: determining a model corresponding to the pollution index monitoring model request from the model database, and determining the model corresponding to the pollution index monitoring model request as a pollution index monitoring model;
the judging result comprises a model corresponding to the pollution index monitoring model request which does not exist in the model database; the model determination module includes: the training device comprises a selection unit, a training unit and a model determination unit; wherein,
The selection unit is used for selecting a combined gas internal combustion engine, the combined gas internal combustion engine passes through the combined learning invitation, and the similarity between the combined gas internal combustion engine and the gas internal combustion engine is not smaller than a preset threshold;
the training unit is used for carrying out joint learning according to respective model training data sets of the joint gas internal combustion engines to obtain each local pollution index monitoring model;
the model determining unit is used for determining a combined pollution index monitoring model according to each local pollution index monitoring model; and determining the combined pollution index monitoring model as a pollution index monitoring model.
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