CN116520781A - Method and system for improving garbage power generation efficiency based on prediction and feedback data - Google Patents
Method and system for improving garbage power generation efficiency based on prediction and feedback data Download PDFInfo
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- 238000002485 combustion reaction Methods 0.000 claims description 6
- 238000004056 waste incineration Methods 0.000 claims description 4
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Classifications
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/33—Director till display
- G05B2219/33273—DCS distributed, decentralised controlsystem, multiprocessor
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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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E20/00—Combustion technologies with mitigation potential
- Y02E20/12—Heat utilisation in combustion or incineration of waste
Abstract
The invention provides a method and a system for improving garbage power generation efficiency based on prediction and feedback data, belonging to the technical field of garbage incineration, wherein the method comprises the following steps: acquiring historical data formed by a DCS control system in a preset time period; preprocessing the historical data to form garbage heating training data based on a DCS control system; training the garbage heating control model based on the training data to form a garbage heating pre-estimation model; acquiring a power supply quantity curve of the heating park so as to calculate the sum of the power consumption of the heating park in the next time period based on the power supply quantity curve; and determining the heating demand of the next time period based on the electricity consumption sum, the thermal demand of the thermal pipeline and the electric power demand of the external network. By adopting the scheme, the thermal efficiency of garbage power generation can be improved.
Description
Technical Field
The invention relates to the technical field of garbage incineration, in particular to a method and a system for improving garbage power generation efficiency based on prediction and feedback data.
Background
In the daily life of people, a large amount of garbage is inevitably generated. If the garbage is not effectively treated, the living environment of people is greatly influenced, and even the health of people is threatened. At present, incineration is one of the main modes for garbage disposal, but in the garbage incineration process, secondary environmental pollution is caused, and resources and energy are wasted. Therefore, in the garbage treatment, a garbage incineration power generation method can be adopted.
Modern refuse incineration power plants have the following characteristics:
1. due to the specificity of the modern garbage incineration power plant, the power generation and supply efficiency of the modern garbage incineration power plant is much lower than that of the modern thermal power plant.
2. Modern garbage incineration power plants are required to strictly meet standard requirements such as GWKB322000 and the like as a first target, and the heat energy utilization rate is improved as much as possible under the conditions of technology and economy.
Therefore, how to improve the power supply efficiency of garbage incineration becomes a problem to be solved.
Disclosure of Invention
In view of the above, embodiments of the present invention provide a method for improving garbage power efficiency based on prediction and feedback data, which at least partially solves the problems existing in the prior art.
In a first aspect, an embodiment of the present invention provides a method for improving garbage power efficiency based on prediction and feedback data, including:
acquiring historical data formed by a DCS control system in a preset time period, wherein the historical data comprises a control parameter set, a garbage incineration parameter set and a garbage calorific value set of the DCS control system;
preprocessing the historical data to form garbage heating training data based on a DCS control system;
training the garbage heating control model based on the training data to form a garbage heating pre-estimation model, wherein the input of the pre-estimation model is the control parameter of a DCS control system and the garbage incineration parameter, and the output position of the pre-estimation model is the garbage heating value;
acquiring a power supply quantity curve of the heating park so as to calculate the sum of the power consumption of the heating park in the next time period based on the power supply quantity curve;
and determining the heating demand of the next time period based on the electricity consumption sum, the heating demand of the heating pipeline and the electric power demand of the external network, so as to determine the control parameters and the garbage incineration parameters of the DCS control system of the next time period based on the heating demand of the next time period.
According to a specific implementation manner of the embodiment of the present disclosure, the obtaining the historical data formed by the DCS control system in the preset time period includes:
acquiring all data generated by a DCS control system in the garbage incineration process within a preset time period;
data screening is carried out on all data generated in the garbage incineration process;
based on the data screening result, a control parameter set, a garbage incineration parameter set and a garbage calorific value set of the DCS control system are generated.
According to a specific implementation manner of the embodiment of the present disclosure, the forming the garbage heating training data based on the DCS control system by preprocessing the history data includes:
performing feature extraction on a control parameter set, a garbage incineration parameter set and a garbage heat productivity set of the DCS control system to generate a plurality of feature vectors related to garbage incineration;
combining the feature vectors according to a preset sequence to form a feature matrix;
and sequencing the feature matrixes according to a time sequence to form garbage heating training data.
According to a specific implementation manner of the embodiment of the disclosure, the feature extraction for the control parameter set, the garbage incineration parameter set and the garbage heat productivity set of the DCS control system includes:
and obtaining the content of the combustible gas formed in the garbage incineration process, and taking the content of the combustible gas as one of the characteristic vectors in the garbage incineration parameter set.
According to a specific implementation manner of the embodiment of the disclosure, the feature extraction is performed on a control parameter set, a garbage incineration parameter set and a garbage heat productivity set of a DCS control system, and further includes:
among DCS control parameters, a control parameter related to a combustion value is extracted as one of feature vectors in a garbage incineration parameter set.
According to a specific implementation manner of the embodiment of the disclosure, the feature extraction is performed on a control parameter set, a garbage incineration parameter set and a garbage heat productivity set of a DCS control system, and further includes:
in the garbage heat generation amount set, the heat generation amount and the humidity value are used as heat generation parameters to perform feature extraction, and the heat generation amount and the humidity value are used as one of feature vectors in the garbage incineration parameter set.
According to a specific implementation manner of the embodiment of the disclosure, the determining the heating demand of the next time period based on the sum of the electricity consumption, the heating demand of the heating power pipeline, and the power demand of the external network, so as to determine the control parameter and the garbage incineration parameter of the DCS control system of the next time period based on the heating demand of the next time period includes:
a historical heating profile of the heating line is obtained in order to determine a thermal demand for a next time period based on the historical heating profile.
According to a specific implementation manner of the embodiment of the disclosure, the determining the heating demand of the next time period based on the sum of the electricity consumption, the heating demand of the heating power pipeline, and the power demand of the external network, so as to determine the control parameter and the garbage incineration parameter of the DCS control system of the next time period based on the heating demand of the next time period, further includes:
and acquiring a historical power supply curve of the external network so as to determine the power demand of the external network in the next time period based on the historical power supply curve of the external network.
According to a specific implementation manner of the embodiment of the disclosure, the determining the heating demand of the next time period based on the sum of the electricity consumption, the heating demand of the heating power pipeline, and the power demand of the external network, so as to determine the control parameter and the garbage incineration parameter of the DCS control system of the next time period based on the heating demand of the next time period, further includes:
after superposition calculation is carried out on the sum of the electricity consumption and the electricity demand of the external network, the electricity demand is converted into power generation heat energy demand;
combining the power generation heat energy demand and the thermal demand of the thermal pipeline into a total heat energy demand;
based on the total heat energy demand, determining control parameters and garbage incineration parameters of a DCS control system in the next time period.
In a second aspect, an embodiment of the present invention provides a system for improving garbage power efficiency based on prediction and feedback data, including:
the acquisition module is used for acquiring historical data formed by the DCS control system in a preset time period, wherein the historical data comprises a control parameter set, a garbage incineration parameter set and a garbage heating value set of the DCS control system;
the processing module is used for preprocessing the historical data to form garbage heating training data based on a DCS control system;
the training module is used for training the garbage heating control model based on the training data to form a garbage heating pre-estimation model, wherein the input of the pre-estimation model is the control parameter and the garbage incineration parameter of the DCS control system, and the output position of the pre-estimation model is the garbage heating value;
the calculation module is used for obtaining a power supply curve of the heating park so as to calculate the sum of the power consumption of the heating park in the next time period based on the power supply curve;
and the determining module is used for determining the heating demand of the next time period based on the electricity consumption sum, the heating demand of the heating pipeline and the electric power demand of the external network so as to determine the control parameters and the garbage incineration parameters of the DCS control system of the next time period based on the heating demand of the next time period.
In a third aspect, an embodiment of the present invention further provides an electronic device, including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of improving garbage power efficiency based on predictive and feedback data in any one of the implementations of the foregoing Ren Di aspect or the first aspect.
In a fourth aspect, embodiments of the present invention further provide a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method for improving garbage power efficiency based on prediction and feedback data in the foregoing first aspect or any implementation manner of the first aspect.
In a fifth aspect, embodiments of the present invention also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the method of improving garbage collection efficiency based on prediction and feedback data in any of the implementations of the first aspect or the first aspect.
The method for improving the garbage power generation efficiency based on the prediction and feedback data provided by the embodiment of the invention comprises the following steps: acquiring historical data formed by a DCS control system in a preset time period, wherein the historical data comprises a control parameter set, a garbage incineration parameter set and a garbage calorific value set of the DCS control system; preprocessing the historical data to form garbage heating training data based on a DCS control system; training the garbage heating control model based on the training data to form a garbage heating pre-estimation model, wherein the input of the pre-estimation model is the control parameter of a DCS control system and the garbage incineration parameter, and the output position of the pre-estimation model is the garbage heating value; acquiring a power supply quantity curve of the heating park so as to calculate the sum of the power consumption of the heating park in the next time period based on the power supply quantity curve; and determining the heating demand of the next time period based on the electricity consumption sum, the heating demand of the heating pipeline and the electric power demand of the external network, so as to determine the control parameters and the garbage incineration parameters of the DCS control system of the next time period based on the heating demand of the next time period. Through the scheme of this application, improved the thermal efficiency of garbage power generation.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a method for improving thermal efficiency of garbage power generation based on prediction and feedback data according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of another method for improving thermal efficiency of garbage power generation based on prediction and feedback data according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of another method for improving thermal efficiency of garbage power generation based on prediction and feedback data according to an embodiment of the present invention; the method comprises the steps of carrying out a first treatment on the surface of the
FIG. 4 is a schematic diagram of a system for improving thermal efficiency of garbage power generation based on prediction and feedback data according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Other advantages and effects of the present disclosure will become readily apparent to those skilled in the art from the following disclosure, which describes embodiments of the present disclosure by way of specific examples. It will be apparent that the described embodiments are merely some, but not all embodiments of the present disclosure. The disclosure may be embodied or practiced in other different specific embodiments, and details within the subject specification may be modified or changed from various points of view and applications without departing from the spirit of the disclosure. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the following claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the present disclosure, one skilled in the art will appreciate that one aspect described herein may be implemented independently of any other aspect, and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. In addition, such apparatus may be implemented and/or such methods practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should also be noted that the illustrations provided in the following embodiments merely illustrate the basic concepts of the disclosure by way of illustration, and only the components related to the disclosure are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided in order to provide a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
Referring to fig. 1, fig. 2 and fig. 3, an embodiment of the present invention provides a method for improving garbage power efficiency based on prediction and feedback data, including:
s101, acquiring historical data formed by a DCS control system in a preset time period, wherein the historical data comprises a control parameter set, a garbage incineration parameter set and a garbage heat productivity set of the DCS control system.
During the use process, the DCS control system can save data acquired during the use process, wherein the data comprise control parameters of the DCS, combustible gases such as CO, NOx and the like generated during the garbage incineration process, garbage humidity values, combustion temperature values and the like. Therefore, a control parameter set, a garbage incineration parameter set and a garbage heat productivity set of the DCS control system can be obtained by collecting the data values.
S102, preprocessing the historical data to form garbage heating training data based on a DCS control system.
The data of the control parameter set, the garbage incineration parameter set and the garbage heat productivity set of the DCS control system belong to original data, the data can be subjected to feature extraction to form feature vectors, feature matrixes are obtained through the feature vectors, and the feature matrixes can be used as training data.
Specifically, the content of the combustible gas formed in the garbage incineration process can be obtained, and the content of the combustible gas is used as one of the characteristic vectors in the garbage incineration parameter set. Among DCS control parameters, a control parameter related to a combustion value is extracted as one of feature vectors in a garbage incineration parameter set. In the garbage heat generation amount set, the heat generation amount and the humidity value are used as heat generation parameters to perform feature extraction, and the heat generation amount and the humidity value are used as one of feature vectors in the garbage incineration parameter set.
And S103, training the garbage heating control model based on the training data to form a garbage heating pre-estimation model, wherein the input of the pre-estimation model is the control parameters of the DCS control system and the garbage incineration parameters, and the output position of the pre-estimation model is the garbage heating value.
The garbage heating pre-estimation model can be a neural network model, the deep neural network model can be a cyclic neural network model or a gate-controlled cyclic neural network model, and the gate-controlled cyclic neural network model can better capture the dependency relationship between long-time historical garbage burning data. The deep neural network model includes at least an input layer, a hidden layer, and an output layer.
A loss function value may be set for a neural network model, and the deep neural network model may be iteratively trained based on the loss function value.
And S104, acquiring a power supply quantity curve of the heating park so as to calculate the sum of the power consumption of the heating park in the next time period based on the power supply quantity curve.
And S105, determining the heating demand of the next time period based on the electricity consumption sum, the heating demand of the heating pipeline and the electric power demand of the external network, so as to determine the control parameters of the DCS control system and the garbage incineration parameters of the next time period based on the heating demand of the next time period.
The sum of the electricity consumption and the electricity demand of the external network can be subjected to superposition calculation, and then the electricity demand is converted into the electricity generation heat energy demand; combining the power generation heat energy demand and the thermal demand of the thermal pipeline into a total heat energy demand; based on the total heat energy demand, determining control parameters and garbage incineration parameters of a DCS control system in the next time period.
As an example, the correlation coefficient r between the heating variable and the control variable of the DCS control system may be calculated using the following formula:
xi, and N represent sample data, sample average value, and sample size of the control variable, yi and sample data and sample average value of the control variable smoke pollutant unstable emission concentration, max and Min represent maximum value and minimum value of the original variable data, respectively, and Sx and Sy represent standard deviation of the heating variable and the control variable smoke pollutant unstable emission concentration, respectively.
As another example, the average thickness H of the garbage layer may be subjected to the averaging process according to the thickness H of the garbage layer to be incinerated, the upper temperature average T1 of the garbage incineration, and the temperature average T2 of the lower layer:
h=a+b+t1+c+t2, where a, b and c are preset weight coefficients.
In the garbage treatment process, the following process flows can be adopted: after the household garbage transport vehicle is weighed by the wagon balance, garbage is dumped into the garbage storage pit through the garbage dumping door. After the household garbage is stored in the garbage storage pit for 3-5 days to remove certain percolate water, the heat value is improved. Meanwhile, the industrial garbage is fully mixed with the household garbage according to the proportion of not more than 30%, the garbage crane sends the mixed garbage to a feeding platform of the incinerator, and after passing through a feeding hopper and a feeding trough, the feeding hopper pushes the garbage to a SITY2000 reverse pushing type mechanical fire grate for drying, burning, ashes burning and cooling. The flue gas generated by combustion enters a waste heat boiler to perform sufficient heat exchange with water in the boiler, superheated steam with medium temperature and medium pressure is generated to enter a steam turbine generator unit to do work to generate electric energy, steam pumped out from a steam turbine enters a heat supply network to supply heat steam to an industrial park, and besides the self-electricity consumption of a power plant, the rest power is completely connected into a power grid system to realize the cogeneration. The residue after the garbage is burnt is collected by a slag dragging machine, pushed out to a slag pit by a push rod, a slag grab bucket crane is arranged above the slag pit, the slag is grabbed onto a small agricultural transport vehicle and is transported to a slag comprehensive treatment shed, and the slag is comprehensively utilized after iron removal, screening and crushing treatment. The flue gas is purified by adopting an SNCR (ammonia water injection) +semi-dry method (lime slurry) +dry method (CaOH) +active carbon injection+cloth bag dust remover system, and is pumped out by an induced draft fan and discharged to the atmosphere through a chimney. The fly ash collected by the spray tower and the bag-type dust collector adopts a process which is more commonly applicable to a waste incineration power plant: "Cement+chelant stabilization method". The technology that cement is used as solidifying material, cement and chelating agent are used as stabilizing material, after the solidifying treatment of cement reaches the relevant standard, the cement is transported to the area compliance method of self-built fly ash landfill for landfill. For the percolate removed from the garbage, according to the water quality, water quantity characteristics and treatment requirements of the percolate of the project and the percolate treatment engineering practice of domestic garbage incineration plants, a process flow of pretreatment, UASB, MBR membrane biochemical reactor and STRO is adopted. The method achieves the water quality standard of industrial water quality for urban sewage recycling (GB/T19923-2005), and is used as the make-up water for the production cooling water to enter the circulating water tank, and meanwhile, a part of concentrated solution is sprayed back to the hearth for burning on the incinerator at a back-spraying interface, and the other part of concentrated solution is used for pulping, so that the concentrated solution is treated completely, and the zero discharge of sewage is realized. The whole process adopts a DCS control system to control dispersion, operation and management.
Referring to fig. 2, according to a specific implementation manner of the embodiment of the disclosure, the obtaining historical data formed by the DCS control system in a preset time period includes:
s201, acquiring all data generated in the garbage incineration process by a DCS control system in a preset time period;
s202, screening all data generated in the garbage incineration process;
s203, based on the data screening result, generating a control parameter set, a garbage incineration parameter set and a garbage heat productivity set of the DCS control system.
Referring to fig. 3, according to a specific implementation manner of an embodiment of the present disclosure, the forming garbage heating training data based on a DCS control system by preprocessing the history data includes:
s301, performing feature extraction on a control parameter set, a garbage incineration parameter set and a garbage calorific value set of a DCS control system to generate a plurality of feature vectors related to garbage incineration;
s302, combining the feature vectors according to a preset sequence to form a feature matrix;
s303, sorting the feature matrixes according to a time sequence to form garbage heating training data.
According to a specific implementation manner of the embodiment of the disclosure, the feature extraction for the control parameter set, the garbage incineration parameter set and the garbage heat productivity set of the DCS control system includes:
and obtaining the content of the combustible gas formed in the garbage incineration process, and taking the content of the combustible gas as one of the characteristic vectors in the garbage incineration parameter set.
According to a specific implementation manner of the embodiment of the disclosure, the feature extraction is performed on a control parameter set, a garbage incineration parameter set and a garbage heat productivity set of a DCS control system, and further includes:
among DCS control parameters, a control parameter related to a combustion value is extracted as one of feature vectors in a garbage incineration parameter set.
According to a specific implementation manner of the embodiment of the disclosure, the feature extraction is performed on a control parameter set, a garbage incineration parameter set and a garbage heat productivity set of a DCS control system, and further includes:
in the garbage heat generation amount set, the heat generation amount and the humidity value are used as heat generation parameters to perform feature extraction, and the heat generation amount and the humidity value are used as one of feature vectors in the garbage incineration parameter set.
According to a specific implementation manner of the embodiment of the disclosure, the determining the heating demand of the next time period based on the sum of the electricity consumption, the heating demand of the heating power pipeline, and the power demand of the external network, so as to determine the control parameter and the garbage incineration parameter of the DCS control system of the next time period based on the heating demand of the next time period includes:
a historical heating profile of the heating line is obtained in order to determine a thermal demand for a next time period based on the historical heating profile.
According to a specific implementation manner of the embodiment of the disclosure, the determining the heating demand of the next time period based on the sum of the electricity consumption, the heating demand of the heating power pipeline, and the power demand of the external network, so as to determine the control parameter and the garbage incineration parameter of the DCS control system of the next time period based on the heating demand of the next time period, further includes:
and acquiring a historical power supply curve of the external network so as to determine the power demand of the external network in the next time period based on the historical power supply curve of the external network.
According to a specific implementation manner of the embodiment of the disclosure, the determining the heating demand of the next time period based on the sum of the electricity consumption, the heating demand of the heating power pipeline, and the power demand of the external network, so as to determine the control parameter and the garbage incineration parameter of the DCS control system of the next time period based on the heating demand of the next time period, further includes:
after superposition calculation is carried out on the sum of the electricity consumption and the electricity demand of the external network, the electricity demand is converted into power generation heat energy demand;
combining the power generation heat energy demand and the thermal demand of the thermal pipeline into a total heat energy demand;
based on the total heat energy demand, determining control parameters and garbage incineration parameters of a DCS control system in the next time period.
Referring to fig. 4, an embodiment of the present invention provides a system 40 for improving thermal efficiency of garbage power generation based on prediction and feedback data, comprising:
the acquisition module 401 is configured to acquire historical data formed by the DCS control system in a preset time period, where the historical data includes a control parameter set, a garbage incineration parameter set, and a garbage heat productivity set of the DCS control system;
the processing module 402 is configured to form garbage heating training data based on a DCS control system by preprocessing the history data;
the training module 403 is configured to train the garbage heating control model based on the training data to form a garbage heating pre-estimation model, where the input of the pre-estimation model is a control parameter of the DCS control system and a garbage incineration parameter, and the output of the pre-estimation model is garbage heating value;
a calculation module 404, configured to obtain a power supply curve of the heating park, so as to calculate a sum of power consumption of the heating park in a next time period based on the power supply curve;
a determining module 405, configured to determine a heating demand of a next time period based on the sum of the electricity consumption, the thermal demand of the thermal pipeline, and the electric demand of the external network, so as to determine a control parameter and a garbage incineration parameter of the DCS control system of the next time period based on the heating demand of the next time period.
Referring to fig. 5, an embodiment of the present invention also provides an electronic device 60, including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of improving garbage power efficiency based on predictive and feedback data in the foregoing method embodiments.
Embodiments of the present invention also provide a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the foregoing method embodiments.
Embodiments of the present invention also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the method of improving garbage power efficiency based on predictive and feedback data in the foregoing method embodiments.
The system of fig. 4 may perform the method of the embodiment of fig. 1-3, and reference is made to the relevant description of the embodiment of fig. 1-3 for parts of this embodiment that are not described in detail. And will not be described in detail herein.
Referring now to fig. 5, a schematic diagram of an electronic device 60 suitable for use in implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 5 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 5, the electronic device 60 may include a processing system (e.g., a central processing unit, a graphics processor, etc.) 601 that may perform various suitable actions and processes in accordance with programs stored in a Read Only Memory (ROM) 602 or loaded from a storage system 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic device 60 are also stored. The processing system 601, ROM 602, and RAM 603 are connected to each other by a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
In general, the following systems may be connected to the I/O interface 605: input system 606 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output system 607 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, etc.; storage system 608 including, for example, magnetic tape, hard disk, etc.; and a communication system 609. The communication system 609 may allow the electronic device 60 to communicate wirelessly or by wire with other devices to exchange data. While fig. 5 shows the electronic device 60 with various systems, it is to be understood that not all of the illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network through the communication system 609, or installed from the storage system 608, or installed from the ROM 602. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing system 601.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising the at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects an internet protocol address from the at least two internet protocol addresses and returns the internet protocol address; receiving an Internet protocol address returned by the node evaluation equipment; wherein the acquired internet protocol address indicates an edge node in the content distribution network.
Alternatively, the computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from the at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The name of the unit does not in any way constitute a limitation of the unit itself, for example the first acquisition unit may also be described as "unit acquiring at least two internet protocol addresses".
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. The method for improving the garbage power generation efficiency based on the prediction and feedback data is characterized by comprising the following steps of:
acquiring historical data formed by a DCS control system in a preset time period, wherein the historical data comprises a control parameter set, a garbage incineration parameter set and a garbage calorific value set of the DCS control system;
preprocessing the historical data to form garbage heating training data based on a DCS control system;
training the garbage heating control model based on the training data to form a garbage heating pre-estimation model, wherein the input of the pre-estimation model is the control parameter of a DCS control system and the garbage incineration parameter, and the output position of the pre-estimation model is the garbage heating value;
acquiring a power supply quantity curve of the heating park so as to calculate the sum of the power consumption of the heating park in the next time period based on the power supply quantity curve;
and determining the heating demand of the next time period based on the electricity consumption sum, the heating demand of the heating pipeline and the electric power demand of the external network, so as to determine the control parameters and the garbage incineration parameters of the DCS control system of the next time period based on the heating demand of the next time period.
2. The method of claim 1, wherein the obtaining historical data formed by the DCS control system over a preset time period comprises:
acquiring all data generated by a DCS control system in the garbage incineration process within a preset time period;
data screening is carried out on all data generated in the garbage incineration process;
based on the data screening result, a control parameter set, a garbage incineration parameter set and a garbage calorific value set of the DCS control system are generated.
3. The method of claim 2, wherein the forming the garbage heat training data based on the DCS control system by preprocessing the history data comprises:
performing feature extraction on a control parameter set, a garbage incineration parameter set and a garbage heat productivity set of the DCS control system to generate a plurality of feature vectors related to garbage incineration;
combining the feature vectors according to a preset sequence to form a feature matrix;
and sequencing the feature matrixes according to a time sequence to form garbage heating training data.
4. The method according to claim 3, wherein the feature extraction of the control parameter set, the garbage incineration parameter set, and the garbage heat productivity set of the DCS control system includes:
and obtaining the content of the combustible gas formed in the garbage incineration process, and taking the content of the combustible gas as one of the characteristic vectors in the garbage incineration parameter set.
5. The method of claim 4, wherein the feature extraction is performed on a control parameter set, a garbage incineration parameter set, and a garbage calorific value set of a DCS control system, further comprising:
among DCS control parameters, a control parameter related to a combustion value is extracted as one of feature vectors in a garbage incineration parameter set.
6. The method of claim 5, wherein the feature extraction is performed on a control parameter set, a garbage incineration parameter set, and a garbage calorific value set of a DCS control system, further comprising:
in the garbage heat generation amount set, the heat generation amount and the humidity value are used as heat generation parameters to perform feature extraction, and the heat generation amount and the humidity value are used as one of feature vectors in the garbage incineration parameter set.
7. The method of claim 6, wherein determining the heating demand for the next time period based on the sum of the electricity consumption, the thermal demand of the thermal line, and the electrical demand of the external network, so as to determine the control parameters of the DCS control system and the waste incineration parameters for the next time period based on the heating demand for the next time period, comprises:
a historical heating profile of the heating line is obtained in order to determine a thermal demand for a next time period based on the historical heating profile.
8. The method of claim 7, wherein determining the heating demand for the next time period based on the sum of the electricity consumption, the thermal demand of the thermal line, and the electrical demand of the external network, so as to determine the control parameters of the DCS control system and the waste incineration parameters for the next time period based on the heating demand for the next time period, further comprises:
and acquiring a historical power supply curve of the external network so as to determine the power demand of the external network in the next time period based on the historical power supply curve of the external network.
9. The method of claim 8, wherein determining the heating demand for the next time period based on the sum of the electricity consumption, the thermal demand of the thermal line, and the electrical demand of the external grid to facilitate determining the control parameters and the waste incineration parameters of the DCS control system for the next time period based on the heating demand for the next time period, further comprises:
after superposition calculation is carried out on the sum of the electricity consumption and the electricity demand of the external network, the electricity demand is converted into power generation heat energy demand;
combining the power generation heat energy demand and the thermal demand of the thermal pipeline into a total heat energy demand;
based on the total heat energy demand, determining control parameters and garbage incineration parameters of a DCS control system in the next time period.
10. A system for improving the thermal efficiency of garbage power generation based on predictive and feedback data, comprising:
the acquisition module is used for acquiring historical data formed by the DCS control system in a preset time period, wherein the historical data comprises a control parameter set, a garbage incineration parameter set and a garbage heating value set of the DCS control system;
the processing module is used for preprocessing the historical data to form garbage heating training data based on a DCS control system;
the training module is used for training the garbage heating control model based on the training data to form a garbage heating pre-estimation model, wherein the input of the pre-estimation model is the control parameter and the garbage incineration parameter of the DCS control system, and the output position of the pre-estimation model is the garbage heating value;
the calculation module is used for obtaining a power supply curve of the heating park so as to calculate the sum of the power consumption of the heating park in the next time period based on the power supply curve;
and the determining module is used for determining the heating demand of the next time period based on the electricity consumption sum, the heating demand of the heating pipeline and the electric power demand of the external network so as to determine the control parameters and the garbage incineration parameters of the DCS control system of the next time period based on the heating demand of the next time period.
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