CN109299834A - A kind of the water process amount prediction technique and device of water treatment plant - Google Patents
A kind of the water process amount prediction technique and device of water treatment plant Download PDFInfo
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Abstract
This application provides the water process amount prediction techniques and device of a kind of water treatment plant, this method comprises: obtaining more target gensets in target thermal power plant in the operation information at target prediction moment；For every target genset, operation information according to the target genset at the target prediction moment is constituted for characterizing the target genset in the target feature vector of target prediction moment operating status；By the target feature vector of the target genset, it is input in pre-generated water intaking prediction model corresponding with the target genset, obtains the target genset in the water consumption of at least one future time instance；According to each target genset in the water consumption of at least one future time instance, determine water treatment plant corresponding with target thermal power plant in the water process amount of each following historical juncture.The embodiment of the present application can according to corresponding water treatment plant of target thermal power plant the water process amount of each following historical juncture come instruct water treatment plant more according to there is perspective management.
Description
Technical field
This application involves thermal power plant's administrative skill fields, in particular to a kind of water process amount prediction side of water treatment plant
Method and device
Background technique
Thermal power plant is using combustible (such as coal) as the factory of fuel production electric energy.Its basic process of production is:
Fuel heating water in burning generates steam, and the chemical energy of fuel is transformed into thermal energy, the rotation of steam pressure pushing turbine, heat
It can be converted into mechanical energy, then steam turbine drives generator rotation, and mechanical energy is transformed into electric energy.Wherein, prime mover is usually
Steam engine or gas turbine, in some lesser power stations, it is also possible to will use internal combustion engine.They be all by using high temperature,
High steam or combustion gas by turbine become the pressure drop of low-pressure air or condensed water during this to generate electricity.
For water as essential resource in thermal power plant's power generation process, the water taken from water head site is to cannot be used directly for thermoelectricity
Factory's power generation requirements, need water treatment plant to handle in advance the water taken from water head site, after obtaining satisfactory water purification, and will
Satisfactory water purification is delivered to each generating set.Current water treatment plant management when, be typically based on instant data into
Capable, so that the lack of control of water treatment plant is perspective, often there are actual conditions and be expected the problem of situation is not inconsistent.
Summary of the invention
In view of this, the water process amount prediction technique for being designed to provide water treatment plant and device of the embodiment of the present application,
Generating set be can determine in the prediction of the water consumption of at least one future time instance, so as to corresponding according to target thermal power plant
Water treatment plant the water process amount of each following historical juncture come instruct water treatment plant more according to there is perspective management.
In a first aspect, the embodiment of the present application provides a kind of water process amount prediction technique of water treatment plant, comprising:
More target gensets in target thermal power plant are obtained in the operation information at target prediction moment；
For target genset described in every, operation information according to the target genset at the target prediction moment,
It constitutes for characterizing the target genset in the target feature vector of target prediction moment operating status；
By the target feature vector of the target genset, it is input to corresponding with the target genset pre-generated
It fetches water in prediction model, obtains the target genset in the water consumption of at least one future time instance；
Water consumption according to each target genset at least one future time instance, the determining and target thermoelectricity
Water process amount of the corresponding water treatment plant of factory in each following historical juncture.
With reference to first aspect, the embodiment of the present application provides the first possible embodiment of first aspect, in which:
The target feature vector by the target genset is input to pre- Mr. corresponding with the target genset
At water intaking prediction model in front of, further includes:
According to the model of the target genset, the water demands forecasting of target genset corresponding with this kind of model is determined
Model.
With reference to first aspect, the embodiment of the present application provides second of possible embodiment of first aspect, in which:
The water intaking prediction model is generated using following manner:
Obtain operation information and each sample power generation of the more sample generating sets at multiple history samples moment
Actual used water amount of the unit at multiple history samples moment；The model of more sample generating sets is identical；
For every sample generating set, operation information according to the sample generating set at multiple history samples moment,
The sample generating set is generated in corresponding feature vector of each history samples moment；
With the more sample generating sets in corresponding feature vector of each history samples moment to input, with each
Actual used water amount of the sample generating set described in platform at multiple history samples moment is output, is generated and more sample generating sets
The corresponding water intaking prediction model of model.
With reference to first aspect, the embodiment of the present application provides the third possible embodiment of first aspect, in which:
The water consumption according to each target genset at least one future time instance, the determining and target
The water process amount of corresponding water treatment plant of thermal power plant, comprising:
For each future time instance, by each target genset the water consumption of the future time instance sum,
It is determined as corresponding water treatment plant of the target thermal power plant in the water process amount of the future time instance.
With reference to first aspect, the embodiment of the present application provides the 4th kind of possible embodiment of first aspect, in which:
Determination water treatment plant corresponding with the target thermal power plant also wraps after the water process amount of each historical juncture
It includes:
According to the water treatment plant in the water process amount of each future time instance, determine in the corresponding flocculant of each future time instance
Dosage.
With reference to first aspect, the embodiment of the present application provides the 5th kind of possible embodiment of first aspect, in which:
Determination water treatment plant corresponding with the target thermal power plant also wraps after the water process amount of each future time instance
It includes:
According to the water treatment plant in the water process amount of each future time instance, determine the water treatment plant in each future time instance
Quantity of wastewater effluent.
With reference to first aspect, the embodiment of the present application provides the 6th kind of possible embodiment of first aspect, in which: institute
It states and determines water treatment plant corresponding with the target thermal power plant after the water process amount of each future time instance, further includes: determine institute
Water treatment plant is stated in the electricity consumption of each future time instance.
With reference to first aspect, the embodiment of the present application provides the 7th kind of possible embodiment of first aspect, in which: institute
It states and determines water treatment plant corresponding with the target thermal power plant after the water process amount of each future time instance, further includes:
According to the specified water process amount of the water treatment plant and the water treatment plant each future time instance water process amount,
Determine the water treatment plant in the remaining water process amount of each future time instance；
According to the water treatment plant in the remaining water process amount of each future time instance, it is determined as at least one target user conveying
The time of water purification and pushing quantity.
Second aspect, the embodiment of the present application provide a kind of water process amount prediction technique of water treatment plant, comprising:
Module is obtained, is believed for obtaining operation of the more target gensets in target thermal power plant at the target prediction moment
Breath；
Target feature vector determining module, for being directed to every target genset, according to the target genset
In the operation information at target prediction moment, constitute for characterizing the target genset in the mesh of target prediction moment operating status
Mark feature vector；
Prediction module, for being input to the target feature vector of the target genset and the target genset pair
In the pre-generated water intaking prediction model answered, the target genset is obtained in the water consumption of at least one future time instance；
Computing module, for, in the water consumption of at least one future time instance, being determined according to each target genset
Water process amount of the water treatment plant corresponding with the target thermal power plant in each following historical juncture.
In conjunction with second aspect, the embodiment of the present application provides the first possible embodiment of second aspect, described pre-
Survey module, be also used to by the target feature vector of the target genset, be input to it is corresponding with the target genset pre-
Before in the water intaking prediction model first generated,
According to the model of the target genset, the water withdrawal prediction of target genset corresponding with this kind of model is determined
Model.
The water process amount prediction technique and device of water treatment plant provided by the embodiments of the present application, using acquisition target thermal power plant
In operation information of the more target gensets at the target prediction moment, be then directed to every target genset, root
Operation information according to the target genset at the target prediction moment is constituted for characterizing the target genset in target prediction
The target feature vector of moment operating status；And according to the target feature vector of the target genset and corresponding water consumption
Prediction model determines each generating set in the water consumption of at least one future time instance, and the target thermal power plant is corresponding net
Water process amount of the water factory in each following historical juncture, so as to according to corresponding water treatment plant of target thermal power plant in each future
The water process amount of historical juncture has perspective management to instruct water treatment plant to carry out more evidence.
Detailed description of the invention
Technical solution in ord to more clearly illustrate embodiments of the present application, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only some embodiments of the application, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 shows a kind of flow chart of the water process amount prediction technique of water treatment plant provided by the embodiment of the present application one；
Fig. 2 shows in the water process amount prediction technique of water treatment plant provided by the embodiment of the present application, construct and each hair
The flow chart of the specific method of the corresponding water intaking prediction model of motor group model；
Fig. 3 shows a kind of flow chart of the water process amount prediction technique of water treatment plant provided by the embodiment of the present application two；
Fig. 4 shows a kind of flow chart of the water process amount prediction technique of water treatment plant provided by the embodiment of the present application three；
Fig. 5 shows a kind of schematic diagram of the water process amount prediction meanss of water treatment plant provided by the embodiment of the present application four；
Fig. 6 shows a kind of schematic diagram of computer equipment provided by the embodiment of the present application five.
Specific embodiment
To keep the purposes, technical schemes and advantages of the embodiment of the present application clearer, below in conjunction with the embodiment of the present application
Middle attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is only
It is some embodiments of the present application, instead of all the embodiments.The application being usually described and illustrated herein in the accompanying drawings is real
The component for applying example can be arranged and be designed with a variety of different configurations.Therefore, below to the application's provided in the accompanying drawings
The detailed description of embodiment is not intended to limit claimed scope of the present application, but is merely representative of the selected reality of the application
Apply example.Based on embodiments herein, those skilled in the art institute obtained without making creative work
There are other embodiments, shall fall in the protection scope of this application.
Thermal power plant includes generating set and the related auxiliary facility around generating set.Water treatment plant is used as to be sent out for thermal power plant
The necessary facility that electricity provides water purification resource needs to carry out fine-grained management.But current water treatment plant lacks the perspective of management,
Often there is display and expected the case where not being inconsistent.
Therefore, the embodiment of the present application provides the water process amount prediction technique and device of a kind of water treatment plant, can be by right
Generating set in the prediction of the water consumption of at least one future time instance, determine water treatment plant at the water of at least one future time instance
Reason amount, so that guidance, which carries out more evidence to water treatment plant, perspective management.
For the water process convenient for understanding the present embodiment, first to a kind of water treatment plant disclosed in the embodiment of the present application
Amount prediction technique describes in detail.
Embodiment one
It is shown in Figure 1, it is the flow chart of the water process amount prediction technique for the water treatment plant that the embodiment of the present application one provides, institute
The method of stating includes step S101~S104, in which:
S101: more target gensets in target thermal power plant are obtained in the operation information at target prediction moment.
When specific implementation, operation information of the target genset at the target prediction moment, including in following information
One or more:
Generating set model, generating set put into effect the time, the actual power generation of generating set, generating set volume
Determine the water saving facility data etc. of generated energy, generating set.
Target thermal power plant can be the thermal power plant that operation has been put into, and can be building and have part generating set
The thermal power plant to put into effect can also be the thermal power plant not built also.The target prediction moment can be the target prediction moment, can also
To be future time instance.
For the target prediction moment be the target prediction moment the case where, target genset generally at the target prediction moment
The generating set of operation has been put into.The operation information of target genset is its real information at the target prediction moment.
For object time be future time instance the case where, target genset generally refers to have been put into the future time instance
The generating set of operation.It should be noted that the generating set of operation has been put into future time instance for this, at the target prediction moment
It is not necessarily the generating set to put into effect.
In that case, the operation information of target genset can be it in the real information of the future time instance, example
Such as when the model of generating set, rated generation amount determine, being in the future time instance is real information；The fortune of generating set
Row information is also possible to it in future time instance and estimates information；Such as actual power is estimated in the future time instance generating set
Amount etc., can be used as and estimate information.
S102: be directed to every target genset, according to the target genset the target prediction moment operation
Information is constituted for characterizing the target genset in the target feature vector of target prediction moment operating status.
When specific implementation, since different generating sets are in the operation information different from target prediction moment, because
This will be based on each generating set in the operation information of target prediction moment operating status, and building is for characterizing target power generation
Unit is all in the target feature vector of target prediction moment operating status.
Specifically, target feature vector can be constructed using following manner:
It is determined to the operating status feature of characterization generating set operating status.For example, generating set model, generating set
Put into effect the time, the actual power generation of generating set, the rated generation amount of generating set, generating set water saving facility section
Water parameter, generating set operational parameter etc..
For each target genset, according to the operation information of the target genset, the target genset is determined
Characteristic value under each operating status feature.
Characteristic value based on the target genset under each operating status feature, building can characterize target power generation
Target feature vector of the unit in the operating status at target prediction moment.
Herein, numerical characteristics its corresponding numerical value that then be used directly is indicated, and category feature is then used it is hot solely
(one-hot) vector of corresponding one 0,1 composition of coding mode, i.e. each category feature, classification number correspond to the dimension of vector
Number, i.e., a classification corresponds to the one-dimensional of vector, when the predetermined registration operation behavioural characteristic is a certain classification, the corresponding vector of the category
Position takes 1, and other parts then all set 0.
S103: the target feature vector of the target genset is input to corresponding with the target genset preparatory
In the water intaking prediction model of generation, the target genset is obtained in the water consumption of at least one future time instance.
In specific implementation, the model of generating set is different, corresponding water intaking prediction model also different from.Therefore,
In another embodiment of the application, by the target feature vector of the target genset, it is input to and the target genset pair
Before in the pre-generated water intaking prediction model answered, further includes: according to the model of the target genset, determining and this kind of type
The water demands forecasting model of number corresponding generating set.
Specifically, shown in Figure 2, it is corresponding with each generating set model that the embodiment of the present application also provides a kind of building
The specific method of water intaking prediction model, comprising:
S201: more sample generating sets are obtained in the operation information and each sample at multiple history samples moment
Actual used water amount of this generating set at multiple history samples moment；The model of more sample generating sets is identical；
S202: be directed to every sample generating set, according to the sample generating set multiple history samples moment operation
Information generates the generating set in corresponding feature vector of each history samples moment.
Specifically, the structure of the feature vector of the building mode of sample generating set feature vector and above-mentioned target genset
It is identical to build mode, details are not described herein.
S203: with more described generating sets in corresponding feature vector of each history samples moment to input,
Actual used water amount with each sample generating set at multiple history samples moment is output, and building generates electricity with more samples
The corresponding water intaking prediction model of the model of unit.
Illustratively, water demands forecasting model includes: Logic Regression Models, autoregression model, moving average model(MA model), returns certainly
Return moving average model(MA model), integrate rolling average autoregression model, EC GARCH, deep learning model,
Decision-tree model, gradient decline tree-model, gradient promote any one in tree-model.
For different water intaking prediction models, there is different model generating methods.But its principle is similar.
Such as Logic Regression Models, autoregression model, moving average model(MA model), ARMA model, integration
For rolling average autoregression model, EC GARCH, the process of model is generated, actually using obtaining
The process of unknown parameter in the characteristic value of the feature vector taken and corresponding water consumption solving model.
Wherein, water intaking prediction model has different model generating methods.But its principle is similar.
Parameter can be with are as follows: weight coefficient corresponding with each element in feature vector and additional coefficient.It is raw to model
At process, the process that as weight coefficient and additional coefficient are solved, namely: by the feature of multiple sample generating sets
Value of the vector as each explanatory variable, using with the actual used water amount at each sample history moment as the value of explained variable,
Calculate the weight coefficient and additional coefficient of each explanatory variable in water intaking prediction model, the water intaking prediction model after being generated.
It specifically, can be by more sample generating sets in multiple history samples when generating water intaking prediction model
The corresponding feature vector at moment constitutes explanatory variable matrix, and the parameter of each explanatory variable is constituted parameter matrix,
Corresponding actual used water amount of different sample history moment is constituted into explained variable matrix, wherein explanatory variable matrix column table
Levy the characteristic value under each operating status feature, a history samples moment of row every generating set of characterization of explanatory variable；
The corresponding parameter of the different explanatory variables of row characterization of parameter matrix.The row of explained variable matrix characterizes each every generating set
Corresponding actual used water amount.It is then based on the explanatory variable matrix, parameter matrix and explained variable matrix of composition, to parameter
Matrix is solved, to obtain water intaking prediction model.
For deep learning model, need to construct deep learning network in advance, then by sample generating set each
Input and the corresponding actual used water of a history samples moment corresponding feature vector as deep learning network
Amount is used as reference result, and the generation for having supervision is carried out to deep learning network, obtains water intaking prediction model.
It is corresponding with the target genset pre-generated being input to the target feature vector of the target genset
Water intaking prediction model in, obtain the target genset after the water consumption of at least one future time instance, the embodiment of the present application
The water process amount prediction technique of one water treatment plant provided further include:
S104: the water consumption according to each target genset at least one future time instance, the determining and mesh
Corresponding water treatment plant of thermal power plant is marked in the water process amount of each following historical juncture.
Herein, for each future time instance, by each target genset the future time instance water consumption
Sum, be determined as corresponding water treatment plant of the target thermal power plant in the water process amount of the future time instance.
The water process amount prediction technique and device of water treatment plant provided by the embodiments of the present application, using acquisition target thermal power plant
In operation information of the more target gensets at the target prediction moment, be then directed to every target genset, root
Operation information according to the target genset at the target prediction moment is constituted for characterizing the target genset in target prediction
The target feature vector of moment operating status；And according to the target feature vector of the target genset and corresponding water consumption
Prediction model determines each generating set in the water consumption of at least one future time instance, and the target thermal power plant is corresponding net
Water process amount of the water factory in each following historical juncture, so as to according to corresponding water treatment plant of target thermal power plant in each future
The water process amount of historical juncture has perspective management to instruct water treatment plant to carry out evidence.
Embodiment two
Fig. 3 shows a kind of schematic diagram of the water process amount prediction technique of water treatment plant of the offer of the embodiment of the present application two.Include:
S301: more target gensets in target thermal power plant are obtained in the operation information at target prediction moment；
S302: be directed to every target genset, according to the target genset the target prediction moment operation
Information is constituted for characterizing the target genset in the target feature vector of target prediction moment operating status；
S303: the target feature vector of the target genset is input to corresponding with the target genset preparatory
In the water intaking prediction model of generation, the target genset is obtained in the water consumption of at least one future time instance；
S304: the water consumption according to each target genset at least one future time instance, the determining and mesh
Corresponding water treatment plant of thermal power plant is marked in the water process amount of each following historical juncture.
Herein, the specific implementation of above-mentioned S301~S304 is similar with above-mentioned S101~S104, and details are not described herein.
S305: it according to the water treatment plant in the water process amount of each future time instance, determines corresponding in each future time instance
The dosage of flocculant.
Herein, the substance that flocculant is used required for being in water treatment procedure, such as alum etc..
In another embodiment, after above-mentioned S304, can also include:
S306: according to the water treatment plant in the water process amount of each future time instance, determine the water treatment plant in each future
The quantity of wastewater effluent at moment.
In specific implementation, water treatment plant can generate a certain amount of sewage when carrying out purified treatment to water, in determination
After the water process amount of each future time instance, it will be able to determine water treatment plant in the quantity of wastewater effluent of each future time instance, so as to
It is enough that sewage discharge, the sewage treatment etc. of future time instance are managed.
In another embodiment, after above-mentioned S304, can also include:
S307: determine the water treatment plant in the electricity consumption of each future time instance.
Herein, water treatment plant can consume electric energy when purified water.In the water process amount that each future time instance has been determined
Afterwards, it will be able to determine that water treatment plant in the electricity consumption of each future time instance, carries out pipe so as to the electricity cost to future time instance
Reason.
Embodiment three
Fig. 4 shows a kind of schematic diagram of the water process amount prediction technique of water treatment plant of the offer of the embodiment of the present application three.Include:
S401: more target gensets in target thermal power plant are obtained in the operation information at target prediction moment；
S402: be directed to every target genset, according to the target genset the target prediction moment operation
Information is constituted for characterizing the target genset in the target feature vector of target prediction moment operating status；
S403: the target feature vector of the target genset is input to corresponding with the target genset preparatory
In the water intaking prediction model of generation, the target genset is obtained in the water consumption of at least one future time instance；
S404: the water consumption according to each target genset at least one future time instance, the determining and mesh
Corresponding water treatment plant of thermal power plant is marked in the water process amount of each following historical juncture.
Herein, the specific implementation of above-mentioned S401~S404 is similar with above-mentioned S101~S104, and details are not described herein.
S405: according to the specified water process amount of the water treatment plant and the water treatment plant at the water of each future time instance
Reason amount determines the water treatment plant in the remaining water process amount of each future time instance；
S406: according to the water treatment plant in the remaining water process amount of each future time instance, it is determined as at least one target use
Family conveys time and the pushing quantity of water purification.
Herein, target user can be in the generating set built, such as currently have generating set investment in thermal power plant
Operation.Need to increase new generating set to expand the production capacity of thermal power plant.It so can be based on to each historical juncture water purification
Prediction of the factory in the water process amount of each future time instance, and the specified water process amount of current water treatment plant are determined to be new
Generating set supplies water, to guarantee the future time instance of new generating set normal operation.
It is also possible to other corollary equipments of thermal power plant, can be with the other users of right and wrong thermal power plant, such as work as thermal power plant
Remaining water withdrawal it is more in the case where, can use thermal power plant remaining water withdrawal be thermal power plant periphery user carry out water conveying
Deng.
Based on the same inventive concept, it is additionally provided in the embodiment of the present application corresponding with the water process amount prediction technique of water treatment plant
Water treatment plant water process amount prediction meanss, the principle and the application solved the problems, such as due to the device in the embodiment of the present application implemented
The water process amount prediction technique of the above-mentioned water treatment plant of example is similar, therefore the implementation of device may refer to the implementation of method, repeats place
It repeats no more.
Example IV
Referring to Figure 5, a kind of signal of the water process amount prediction meanss of the water treatment plant provided for the embodiment of the present application five
Figure, described device include: to obtain module 51, feature vector generation module 52, prediction module 53 and computing module 54.
Wherein, module 51 is obtained, for obtaining more target gensets in target thermal power plant at the target prediction moment
Operation information；
Feature vector determining module 52 exists for being directed to every target genset according to the target genset
The operation information at target prediction moment is constituted for characterizing the target genset in the target of target prediction moment operating status
Feature vector；
Prediction module 53, for being input to the target feature vector of the target genset and the target genset
In corresponding pre-generated water intaking prediction model, the target genset is obtained in the water consumption of at least one future time instance；
Computing module 54, for according to each target genset at least one future time instance water consumption, really
Water process amount of the fixed water treatment plant corresponding with the target thermal power plant in each following historical juncture.
In a kind of optional embodiment, the prediction module 53 is also used to the target of the target genset is special
Vector is levied, before being input in pre-generated water intaking prediction model corresponding with the target genset,
According to the model of the target genset, the water withdrawal prediction of target genset corresponding with this kind of model is determined
Model.
In a kind of optional embodiment, further includes: model generation module 55, it is pre- for generating water intaking using following manner
Survey model:
Obtain operation information and each sample power generation of the more sample generating sets at multiple history samples moment
Actual used water amount of the unit at multiple history samples moment；The model of more sample generating sets is identical；
For every sample generating set, operation information according to the sample generating set at multiple history samples moment,
The sample generating set is generated in corresponding feature vector of each history samples moment；
With the more sample generating sets in corresponding feature vector of each history samples moment to input, with each
Actual used water amount of the sample generating set described in platform at multiple history samples moment is output, is generated and more sample generating sets
The corresponding water intaking prediction model of model.
In a kind of optional embodiment, computing module 54 is specifically used for being sent out using following manner according to each target
Motor group determines the water process amount of water treatment plant corresponding with the target thermal power plant in the water consumption of at least one future time instance:
For each future time instance, by each target genset the water consumption of the future time instance sum,
It is determined as corresponding water treatment plant of the target thermal power plant in the water process amount of the future time instance.
In a kind of optional embodiment, further includes: first processing module 56,
The first processing module, for determining water treatment plant corresponding with the target thermal power plant in each historical juncture
After water process amount, according to the water treatment plant in the water process amount of each future time instance, determine corresponding in each future time instance
The dosage of flocculant.
In a kind of optional embodiment, further includes: Second processing module 57, for the determining and target thermal power plant pair
The water treatment plant answered after the water process amount of each historical juncture, according to the water treatment plant each future time instance water process
Amount, determines the water treatment plant in the quantity of wastewater effluent of each future time instance.
In a kind of optional embodiment, further includes: third processing module 58, for the determining and target thermal power plant pair
The water treatment plant answered determines the water treatment plant in the electricity consumption of each future time instance after the water process amount of each historical juncture.
In a kind of optional embodiment, further includes: fourth processing module 59, for the determining and target thermal power plant pair
The water treatment plant answered is after the water process amount of each historical juncture, according to the specified water process amount of the water treatment plant and described
Water treatment plant determines the water treatment plant in the remaining water process amount of each future time instance in the water process amount of each future time instance；
According to the water treatment plant in the remaining water process amount of each future time instance, it is determined as at least one target user conveying
The time of water purification and pushing quantity.
The water process amount prediction technique and device of water treatment plant provided by the embodiments of the present application, using acquisition target thermal power plant
In operation information of the more target gensets at the target prediction moment, be then directed to every target genset, root
Operation information according to the target genset at the target prediction moment is constituted for characterizing the target genset in target prediction
The target feature vector of moment operating status；And according to the target feature vector of the target genset and corresponding water consumption
Prediction model determines each generating set in the water consumption of at least one future time instance, and the target thermal power plant is corresponding net
Water process amount of the water factory in each following historical juncture, so as to according to corresponding water treatment plant of target thermal power plant in each future
The water process amount of historical juncture has perspective management to instruct water treatment plant to carry out evidence.
Embodiment five
Corresponding to the water process amount prediction technique of the water treatment plant in Fig. 1, the embodiment of the present application also provides a kind of computers
Equipment 600, as shown in fig. 6, being 600 structural schematic diagram of computer equipment provided by the embodiments of the present application, comprising:
Processor 61, memory 62 and bus 63；Memory 62 is executed instruction for storing, including memory 621 and outside
Memory 622；Here memory 621 is also referred to as built-in storage, for temporarily storing the operational data in processor 61, and with it is hard
The data that the external memories such as disk 622 exchange, processor 61 carry out data exchange by memory 621 and external memory 622, when
When the user equipment 60 is run, communicated between the processor 61 and the memory 62 by bus 63, so that the place
Device 61 is managed to execute in User space to give an order:
More target gensets in target thermal power plant are obtained in the operation information at target prediction moment；
For target genset described in every, operation information according to the target genset at the target prediction moment,
It constitutes for characterizing the target genset in the target feature vector of target prediction moment operating status；
By the target feature vector of the target genset, it is input to corresponding with the target genset pre-generated
It fetches water in prediction model, obtains the target genset in the water consumption of at least one future time instance；
Water consumption according to each target genset at least one future time instance, the determining and target thermoelectricity
Water process amount of the corresponding water treatment plant of factory in each following historical juncture.
In a kind of possible embodiment, in the instruction that processor 61 executes,
The target feature vector by the target genset is input to pre- Mr. corresponding with the target genset
At water intaking prediction model in front of, further includes:
According to the model of the target genset, the water demands forecasting of target genset corresponding with this kind of model is determined
Model.
In a kind of possible embodiment, in the instruction that processor 61 executes,
The water intaking prediction model is generated using following manner:
Obtain operation information and each sample power generation of the more sample generating sets at multiple history samples moment
Actual used water amount of the unit at multiple history samples moment；The model of more sample generating sets is identical；
For every sample generating set, operation information according to the sample generating set at multiple history samples moment,
The sample generating set is generated in corresponding feature vector of each history samples moment；
With the more sample generating sets in corresponding feature vector of each history samples moment to input, with each
Actual used water amount of the sample generating set described in platform at multiple history samples moment is output, is generated and more sample generating sets
The corresponding water intaking prediction model of model.
In a kind of possible embodiment, in the instruction that processor 61 executes,
The water consumption according to each target genset at least one future time instance, the determining and target
The water process amount of corresponding water treatment plant of thermal power plant, comprising:
For each future time instance, by each target genset the water consumption of the future time instance sum,
It is determined as corresponding water treatment plant of the target thermal power plant in the water process amount of the future time instance.
In a kind of possible embodiment, in the instruction that processor 61 executes,
Determination water treatment plant corresponding with the target thermal power plant also wraps after the water process amount of each historical juncture
It includes:
According to the water treatment plant in the water process amount of each future time instance, determine in the corresponding flocculant of each future time instance
Dosage.
In a kind of possible embodiment, in the instruction that processor 61 executes, the determination and the target thermal power plant pair
The water treatment plant answered is after the water process amount of each future time instance, further includes:
According to the water treatment plant in the water process amount of each future time instance, determine the water treatment plant in each future time instance
Quantity of wastewater effluent.
In a kind of possible embodiment, in the instruction that processor 61 executes, the determination and the target thermal power plant pair
The water treatment plant answered is after the water process amount of each future time instance, further includes: determines the water treatment plant in each future time instance
Electricity consumption.
In a kind of possible embodiment, in the instruction that processor 61 executes, the determination and the target thermal power plant pair
The water treatment plant answered is after the water process amount of each future time instance, further includes:
According to the specified water process amount of the water treatment plant and the water treatment plant each future time instance water process amount,
Determine the water treatment plant in the remaining water process amount of each future time instance；
According to the water treatment plant in the remaining water process amount of each future time instance, it is determined as at least one target user conveying
The time of water purification and pushing quantity.
In addition, the embodiment of the present application also provides a kind of computer readable storage medium, on the computer readable storage medium
It is stored with computer program, water treatment plant described in above method embodiment is executed when which is run by processor
The step of water process amount prediction technique.
The computer program product of route planning method provided by the embodiment of the present application, including storing program code
Computer readable storage medium, the instruction that said program code includes can be used for executing water purification described in above method embodiment
The step of water process amount prediction technique of factory, for details, reference can be made to above method embodiments, and details are not described herein.
The water process amount prediction technique of water treatment plant provided by the embodiment of the present application and the computer program product of device,
Computer readable storage medium including storing program code, the instruction that said program code includes can be used for executing front side
Method method as described in the examples, specific implementation can be found in embodiment of the method, and details are not described herein.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description
It with the specific work process of device, can refer to corresponding processes in the foregoing method embodiment, details are not described herein.In the application
In provided several embodiments, it should be understood that disclosed systems, devices and methods, it can be real by another way
It is existing.The apparatus embodiments described above are merely exemplary, for example, the division of the unit, only a kind of logic function
It can divide, there may be another division manner in actual implementation, in another example, multiple units or components can combine or can collect
At another system is arrived, or some features can be ignored or not executed.Another point, shown or discussed mutual coupling
Conjunction or direct-coupling or communication connection can be the indirect coupling or communication connection by some communication interfaces, device or unit,
It can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, each functional unit in each embodiment of the application can integrate in one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.
It, can be with if the function is realized in the form of SFU software functional unit and when sold or used as an independent product
It is stored in the executable non-volatile computer-readable storage medium of a processor.Based on this understanding, the application
Technical solution substantially the part of the part that contributes to existing technology or the technical solution can be with software in other words
The form of product embodies, which is stored in a storage medium, including some instructions use so that
One computer equipment (can be personal computer, server or the network equipment etc.) executes each embodiment institute of the application
State all or part of the steps of method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (Read-Only
Memory, ROM), random access memory (Random Access Memory, RAM), magnetic or disk etc. is various to deposit
Store up the medium of program code.
Finally, it should be noted that embodiment described above, the only specific embodiment of the application, to illustrate the application
Technical solution, rather than its limitations, the protection scope of the application is not limited thereto, although with reference to the foregoing embodiments to this Shen
It please be described in detail, those skilled in the art should understand that: anyone skilled in the art
Within the technical scope of the present application, it can still modify to technical solution documented by previous embodiment or can be light
It is readily conceivable that variation or equivalent replacement of some of the technical features；And these modifications, variation or replacement, do not make
The essence of corresponding technical solution is detached from the spirit and scope of the embodiment of the present application technical solution, should all cover the protection in the application
Within the scope of.Therefore, the protection scope of the application shall be subject to the protection scope of the claim.
Claims (10)
1. a kind of water process amount prediction technique of water treatment plant characterized by comprising
More target gensets in target thermal power plant are obtained in the operation information at target prediction moment；
For target genset described in every, operation information according to the target genset at the target prediction moment is constituted
For characterizing the target genset in the target feature vector of target prediction moment operating status；
By the target feature vector of the target genset, it is input to pre-generated water intaking corresponding with the target genset
In prediction model, the target genset is obtained in the water consumption of at least one future time instance；
Water consumption according to each target genset at least one future time instance, the determining and target thermal power plant pair
Water process amount of the water treatment plant answered in each following historical juncture.
2. the method according to claim 1, wherein the target feature vector by the target genset,
Before being input in pre-generated water intaking prediction model corresponding with the target genset, further includes:
According to the model of the target genset, the water demands forecasting mould of target genset corresponding with this kind of model is determined
Type.
3. according to the method described in claim 2, it is characterized in that, generating the water intaking prediction model using following manner:
More sample generating sets are obtained in the operation information and each sample generating set at multiple history samples moment
In the actual used water amount at multiple history samples moment；The model of more sample generating sets is identical；
For every sample generating set, operation information according to the sample generating set at multiple history samples moment is generated
The sample generating set is in corresponding feature vector of each history samples moment；
With the more sample generating sets in corresponding feature vector of each history samples moment to input, with each institute
The actual used water amount that sample generating set is stated at multiple history samples moment is output, generates the type with more sample generating sets
Number corresponding water intaking prediction model.
4. the method according to claim 1, wherein it is described according to each target genset at least one
The water consumption of a future time instance determines the water process amount of water treatment plant corresponding with the target thermal power plant, comprising:
It is determined for each future time instance by each target genset in the sum of the water consumption of the future time instance
For corresponding water treatment plant of the target thermal power plant the future time instance water process amount.
5. the method according to claim 1, wherein determination water treatment plant corresponding with the target thermal power plant
After the water process amount of each historical juncture, further includes:
According to the water treatment plant in the water process amount of each future time instance, the use in the corresponding flocculant of each future time instance is determined
Amount.
6. the method according to claim 1, wherein determination water treatment plant corresponding with the target thermal power plant
After the water process amount of each future time instance, further includes:
According to the water treatment plant in the water process amount of each future time instance, determine the water treatment plant in the sewage of each future time instance
Discharge amount.
7. the method according to claim 1, wherein determination water treatment plant corresponding with the target thermal power plant
After the water process amount of each future time instance, further includes: determine the water treatment plant in the electricity consumption of each future time instance.
8. the method according to claim 1, wherein determination water treatment plant corresponding with the target thermal power plant
After the water process amount of each future time instance, further includes:
According to the specified water process amount of the water treatment plant and the water treatment plant in the water process amount of each future time instance, determine
Remaining water process amount of the water treatment plant in each future time instance；
According to the water treatment plant in the remaining water process amount of each future time instance, it is determined as at least one target user and conveys water purification
Time and pushing quantity.
9. a kind of water process amount prediction technique of water treatment plant characterized by comprising
Module is obtained, for obtaining more target gensets in target thermal power plant in the operation information at target prediction moment；
Feature vector determining module, it is pre- in target according to the target genset for being directed to every target genset
Survey the moment operation information, constitute for characterize the target genset target prediction moment operating status target signature to
Amount；
Prediction module, it is corresponding with the target genset for being input to the target feature vector of the target genset
In pre-generated water intaking prediction model, the target genset is obtained in the water consumption of at least one future time instance；
Computing module, for the water consumption according to each target genset at least one future time instance, determining and institute
Corresponding water treatment plant of target thermal power plant is stated in the water process amount of each following historical juncture.
10. device according to claim 9, which is characterized in that the prediction module is also used to by the target generator
The target feature vector of group, before being input in pre-generated water intaking prediction model corresponding with the target genset,
According to the model of the target genset, determine that the water withdrawal of target genset corresponding with this kind of model predicts mould
Type.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110092507A (en) * | 2019-05-30 | 2019-08-06 | 中国水利水电科学研究院 | A kind of method and device of Industrial Wastewater Treatment |
| CN110135661A (en) * | 2019-05-30 | 2019-08-16 | 中国水利水电科学研究院 | A kind of the water process amount prediction technique and device of fired power generating unit desalted water station |
| CN110175934A (en) * | 2019-05-30 | 2019-08-27 | 中国水利水电科学研究院 | A kind of middle water concocting method and device |
| CN115018212A (en) * | 2022-08-08 | 2022-09-06 | 水利部珠江水利委员会珠江水利综合技术中心 | Power generation water consumption prediction analysis method and system and cloud platform |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105677791A (en) * | 2015-12-31 | 2016-06-15 | 新疆金风科技股份有限公司 | Method and system used for analyzing operating data of wind generating set |
-
2018
- 2018-12-03 CN CN201811465094.4A patent/CN109299834A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105677791A (en) * | 2015-12-31 | 2016-06-15 | 新疆金风科技股份有限公司 | Method and system used for analyzing operating data of wind generating set |
Non-Patent Citations (1)
| Title |
|---|
| 沈明忠 等: "热力发电环境污染与耗水率预测模型分析", 《华电技术》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110092507A (en) * | 2019-05-30 | 2019-08-06 | 中国水利水电科学研究院 | A kind of method and device of Industrial Wastewater Treatment |
| CN110135661A (en) * | 2019-05-30 | 2019-08-16 | 中国水利水电科学研究院 | A kind of the water process amount prediction technique and device of fired power generating unit desalted water station |
| CN110175934A (en) * | 2019-05-30 | 2019-08-27 | 中国水利水电科学研究院 | A kind of middle water concocting method and device |
| CN110175934B (en) * | 2019-05-30 | 2021-06-25 | 中国水利水电科学研究院 | Reclaimed water blending method and device |
| CN115018212A (en) * | 2022-08-08 | 2022-09-06 | 水利部珠江水利委员会珠江水利综合技术中心 | Power generation water consumption prediction analysis method and system and cloud platform |
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