CN114418358A - Boiler scheduling method and device - Google Patents
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Abstract
The disclosure relates to the technical field of artificial intelligence, and provides a boiler scheduling method and device. The method comprises the following steps: acquiring current data of a boiler group corresponding to the boiler group, wherein the current data of the boiler group comprises the current data of a boiler corresponding to each boiler in the boiler group; generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group; each boiler in the boiler group is controlled by a boiler scheduling command. By adopting the technical means, the problem that automatic scheduling of the boiler cannot be realized in the prior art is solved.
Description
Technical Field
The disclosure relates to the technical field of boilers, in particular to a boiler scheduling method and device.
Background
With the continuous increase of industrial steam and centralized heating demands, boiler groups of heating stations with a plurality of steam boilers are reasonably scheduled, so that the key problems of lowest energy consumption and highest efficiency are achieved on the premise of meeting the steam demand of users. However, in the prior art, the research on the aspect of boiler group scheduling optimization is less, the strategy is customized for the heating plant mainly by the subjective experience of field operators, and the optimal scheduling state cannot be achieved frequently.
In the course of implementing the disclosed concept, the inventors found that there are at least the following technical problems in the related art: the automatic scheduling of the boiler can not be realized.
Disclosure of Invention
In view of this, embodiments of the present disclosure provide a boiler scheduling method, a boiler scheduling device, an electronic device, and a computer-readable storage medium, so as to solve the problem in the prior art that automatic scheduling of a boiler cannot be implemented.
In a first aspect of the disclosed embodiments, a boiler scheduling method is provided, including: acquiring current data of a boiler group corresponding to the boiler group, wherein the current data of the boiler group comprises the current data of a boiler corresponding to each boiler in the boiler group; generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group; each boiler in the boiler group is controlled by a boiler scheduling command.
In a second aspect of the disclosed embodiments, a boiler scheduling apparatus is provided, including: the acquisition module is configured to acquire boiler group current data corresponding to a boiler group, wherein the boiler group current data comprises boiler current data corresponding to each boiler in the boiler group; the generating module is configured to generate boiler scheduling instructions by using an evolutionary algorithm according to the current data of the boiler group; a control module configured to control each boiler in the boiler group via boiler scheduling instructions.
In a third aspect of the embodiments of the present disclosure, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.
In a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, which stores a computer program, which when executed by a processor, implements the steps of the above-mentioned method.
Compared with the prior art, the embodiment of the disclosure has the following beneficial effects: acquiring current data of a boiler group corresponding to the boiler group, wherein the current data of the boiler group comprises the current data of a boiler corresponding to each boiler in the boiler group; generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group; each boiler in the boiler group is controlled by a boiler scheduling command. By adopting the technical means, the problem that automatic scheduling of the boiler cannot be realized in the prior art can be solved, and further, the method for automatically scheduling the boiler is provided.
Drawings
To more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without inventive efforts.
FIG. 1 is a scenario diagram of an application scenario of an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart diagram illustrating a boiler scheduling method according to an embodiment of the present disclosure;
FIG. 3 is a schematic structural diagram of a boiler scheduling apparatus according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the disclosed embodiments. However, it will be apparent to one skilled in the art that the present disclosure may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present disclosure with unnecessary detail.
A boiler scheduling method and apparatus according to an embodiment of the present disclosure will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a scene schematic diagram of an application scenario of an embodiment of the present disclosure. The application scenario may include terminal devices 1, 2, and 3, server 4, and network 5.
The terminal devices 1, 2, and 3 may be hardware or software. When the terminal devices 1, 2 and 3 are hardware, they may be various electronic devices having a display screen and supporting communication with the server 4, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like; when the terminal devices 1, 2, and 3 are software, they may be installed in the electronic devices as above. The terminal devices 1, 2 and 3 may be implemented as a plurality of software or software modules, or may be implemented as a single software or software module, which is not limited by the embodiments of the present disclosure. Further, the terminal devices 1, 2, and 3 may have various applications installed thereon, such as a data processing application, an instant messaging tool, social platform software, a search-type application, a shopping-type application, and the like.
The server 4 may be a server providing various services, for example, a backend server receiving a request sent by a terminal device establishing a communication connection with the server, and the backend server may receive and analyze the request sent by the terminal device and generate a processing result. The server 4 may be one server, may also be a server cluster composed of a plurality of servers, or may also be a cloud computing service center, which is not limited in this disclosure.
The server 4 may be hardware or software. When the server 4 is hardware, it may be various electronic devices that provide various services to the terminal devices 1, 2, and 3. When the server 4 is software, it may be a plurality of software or software modules providing various services for the terminal devices 1, 2, and 3, or may be a single software or software module providing various services for the terminal devices 1, 2, and 3, which is not limited by the embodiment of the present disclosure.
The network 5 may be a wired network connected by a coaxial cable, a twisted pair and an optical fiber, or may be a wireless network that can interconnect various Communication devices without wiring, for example, Bluetooth (Bluetooth), Near Field Communication (NFC), Infrared (Infrared), and the like, which is not limited in the embodiment of the present disclosure.
A user can establish a communication connection with the server 4 via the network 5 through the terminal devices 1, 2, and 3 to receive or transmit information or the like. It should be noted that the specific types, numbers and combinations of the terminal devices 1, 2 and 3, the server 4 and the network 5 may be adjusted according to the actual requirements of the application scenarios, and the embodiment of the present disclosure does not limit this.
FIG. 2 is a schematic flow chart diagram illustrating a boiler scheduling method according to an embodiment of the present disclosure. The boiler scheduling method of fig. 2 may be performed by the terminal device or the server of fig. 1. As shown in fig. 2, the boiler scheduling method includes:
s201, obtaining current data of boiler groups corresponding to the boiler groups, wherein the current data of the boiler groups comprises the current data of boilers corresponding to each boiler in the boiler groups;
s202, generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group;
and S203, controlling each boiler in the boiler group through a boiler scheduling command.
The boiler group current data is data of the current operation of the boiler group, including the energy currently provided by the boiler group, i.e., the current load of the boiler group. The boiler group includes a plurality of boilers. And the current data of the boiler corresponding to each boiler in the boiler group comprises the current load of each boiler. The boiler scheduling instructions are for adjusting the load of each boiler in the group of boilers. For example, the boiler group comprises A, B boilers and C boilers, wherein the load of the A boiler is the largest, the load of the C boiler is the smallest, and the load of the A boiler can be reduced and the load of the C boiler can be increased for safety.
Evolutionary algorithms, or "evolutionary algorithms" (EAs), are a cluster of algorithms that have created a sense of inspiration from the biological evolution of nature, despite many variations, different patterns of genetic expression, different crossover and mutation operators, the introduction of special operators, and different regeneration and selection methods. Compared with the traditional optimization algorithms such as a calculus-based method and an exhaustion method, the evolutionary computation is a mature global optimization method with high robustness and wide applicability, has the characteristics of self-organization, self-adaptation and self-learning, can not be limited by problem properties, and effectively processes the complex problems which are difficult to solve by the traditional optimization algorithms.
According to the technical scheme provided by the embodiment of the disclosure, the current data of the boiler groups corresponding to the boiler groups are obtained, wherein the current data of the boiler groups comprise the current data of the boiler corresponding to each boiler in the boiler groups; generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group; each boiler in the boiler group is controlled by a boiler scheduling command. By adopting the technical means, the problem that automatic scheduling of the boiler cannot be realized in the prior art can be solved, and further, the method for automatically scheduling the boiler is provided.
In step S202, a boiler scheduling command is generated by using an evolutionary algorithm according to the current data of the boiler group, including: performing data preprocessing on current data of the boiler group, wherein the data preprocessing comprises the following steps: deleting interference data in the current data of the boiler group; and generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group after data preprocessing.
Because the steam flow still exists in the boiler for a period of time after the boiler is stopped, the interference data can be load data corresponding to the steam flow existing in the boiler after the boiler is stopped. And a more accurate boiler scheduling instruction can be generated through the current data of the boiler group after data preprocessing.
In step S202, a boiler scheduling command is generated by using an evolutionary algorithm according to the current data of the boiler group, including: acquiring boiler group historical data corresponding to a boiler group, wherein the boiler group historical data comprises boiler historical data corresponding to each boiler in the boiler group; processing historical data of the boiler group by using a load prediction algorithm to obtain a load prediction result; and generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data and the load prediction result of the boiler group.
The boiler group history data is data that the boiler group has historically operated, including the energy provided by the boiler group history, i.e., the total load in the boiler group history. The boiler group includes a plurality of boilers. And boiler history data corresponding to each boiler in the boiler group comprises the total load in each boiler history.
Acquiring training data, wherein the training data comprises boiler group historical data corresponding to a plurality of boiler groups; and training the first neural network model by using the training data, so that the first neural network model learns and saves the corresponding relation between the data of the operation of the boiler group and the load prediction result. The load prediction algorithm may be understood as a trained first neural network model.
And generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group and the load prediction result.
In step S202, after the historical data of the boiler group is processed by using a load prediction algorithm to obtain a load prediction result, the method further comprises: calculating the boiler characteristics of each boiler in the boiler group by utilizing a characteristic learning algorithm according to the historical data of the boiler corresponding to each boiler in the boiler group; and generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group, the load prediction result and the boiler characteristics of each boiler in the boiler group.
Acquiring training data, wherein the training data comprises boiler group historical data corresponding to a plurality of boiler groups; and training a second neural network model by using the training data, so that the second neural network model learns and saves the corresponding relation between the operating data of the boiler group and the boiler characteristics of each boiler in the boiler group. The feature learning algorithm may be understood as a trained second neural network model. The first neural network model and the second neural network model may be the same neural network model. The two training sessions above are different mainly for different purposes, or different labels are given to the training data. Before training the neural network model, the training data should be labeled according to the training purpose. The boiler characteristic of a boiler may be properties of the boiler such as the maximum load and the most suitable load of the boiler, etc., and a most suitable load of a boiler is the load at the optimum state of operation of the boiler. The load prediction result is a predicted load that the boiler group needs to bear in a period of time, and the current data of the boiler group comprises the current load of the boiler group. And generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group, the load prediction result and the boiler characteristics of each boiler in the boiler group.
In step S202, a boiler scheduling command is generated by using an evolutionary algorithm according to the current data of the boiler group, including: acquiring boiler group historical data corresponding to a boiler group, wherein the boiler group historical data comprises boiler historical data corresponding to each boiler in the boiler group; calculating the boiler characteristics of each boiler in the boiler group by utilizing a characteristic learning algorithm according to the historical data of the boiler corresponding to each boiler in the boiler group; and generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group and the boiler characteristics of each boiler in the boiler group.
The boiler characteristic of a boiler may be an attribute of the boiler, the load prediction result is a predicted load that a group of boilers needs to bear over a period of time, and the current data of the group of boilers includes a current load of the group of boilers. And generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group and the boiler characteristics of each boiler in the boiler group.
After the boiler characteristics of each boiler in the boiler group are calculated by using a characteristic learning algorithm according to the historical boiler data corresponding to each boiler in the boiler group, the method further comprises the following steps: setting an independent variable of an objective function, wherein the evolutionary algorithm comprises the objective function; acquiring a set function instruction, and generating a constraint condition of an independent variable according to the set function instruction and the boiler characteristics of each boiler in a boiler group; processing historical data of the boiler group by using a load prediction algorithm to obtain a load prediction result; and generating constraint conditions of the target function according to the set function instruction and the load prediction result.
Generating an independent variable constraint condition:
let the argument be x1,x2,...,xnCorresponding boiler characteristic k1,k2,...,kn(ii) a The corresponding load value of the ith boiler is as follows: y isi＝ki*xiThe corresponding standard load is
Wherein the standard load is understood to mean the load of the boiler before it is adjustedThe most initial load of the boiler; the maximum lifting load rate is M, and the limited opening time of frequent start and stop is SiThe frequent start-stop limit closing time is PiThe boiler keeps the current state for Ti
The load lifting rate of a single boiler is within a set range:
the state of 1 boiler is changed at most once:
wherein sign is a sign function.
The boiler with the change of start-up and shut-down needs to meet the set time threshold:
generated objective function constraints:
sum of variables O1And (3) minimizing:
total load O of the load of the boiler to be adjusted2Minimum:
the evolution algorithm related parameters also need to be set: according to the number of the boilers, parameters such as the scale of a boiler group, the maximum evolution algebra and the recombination probability are set, and then the optimal solution is obtained through an evolutionary algorithm. The above parameters are common parameters in the evolutionary algorithm, and are not described herein again. The optimal solution may be understood as an optimal solution for scheduling each boiler in the group of boilers.
Setting arguments of an objective function, including: and setting the value range, the upper boundary, the lower boundary, the dimension and the type of the independent variable.
The setting may be understood as setting an attribute of an argument, where the attribute of the argument is a common attribute in mathematical computation and is not described herein again.
All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described herein again.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 3 is a schematic diagram of a boiler scheduling apparatus provided in an embodiment of the present disclosure. As shown in fig. 3, the boiler scheduling apparatus includes:
an obtaining module 301, configured to obtain boiler group current data corresponding to a boiler group, where the boiler group current data includes boiler current data corresponding to each boiler in the boiler group;
a generating module 302 configured to generate a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group;
a control module 303 configured to control each boiler in the boiler group by boiler scheduling instructions.
The boiler group current data is data of the current operation of the boiler group, including the energy currently provided by the boiler group, i.e., the current load of the boiler group. The boiler group includes a plurality of boilers. And the current data of the boiler corresponding to each boiler in the boiler group comprises the current load of each boiler. The boiler scheduling instructions are for adjusting the load of each boiler in the group of boilers. For example, the boiler group comprises A, B boilers and C boilers, wherein the load of the A boiler is the largest, the load of the C boiler is the smallest, and the load of the A boiler can be reduced and the load of the C boiler can be increased for safety.
Evolutionary algorithms, or "evolutionary algorithms" (EAs), are a cluster of algorithms that have created a sense of inspiration from the biological evolution of nature, despite many variations, different patterns of genetic expression, different crossover and mutation operators, the introduction of special operators, and different regeneration and selection methods. Compared with the traditional optimization algorithms such as a calculus-based method and an exhaustion method, the evolutionary computation is a mature global optimization method with high robustness and wide applicability, has the characteristics of self-organization, self-adaptation and self-learning, can not be limited by problem properties, and effectively processes the complex problems which are difficult to solve by the traditional optimization algorithms.
According to the technical scheme provided by the embodiment of the disclosure, the current data of the boiler groups corresponding to the boiler groups are obtained, wherein the current data of the boiler groups comprise the current data of the boiler corresponding to each boiler in the boiler groups; generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group; each boiler in the boiler group is controlled by a boiler scheduling command. By adopting the technical means, the problem that automatic scheduling of the boiler cannot be realized in the prior art can be solved, and further, the method for automatically scheduling the boiler is provided.
Optionally, the generating module 302 is further configured to perform data preprocessing on the current data of the boiler group, wherein the data preprocessing includes: deleting interference data in the current data of the boiler group; and generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group after data preprocessing.
Because the steam flow still exists in the boiler for a period of time after the boiler is stopped, the interference data can be load data corresponding to the steam flow existing in the boiler after the boiler is stopped. And a more accurate boiler scheduling instruction can be generated through the current data of the boiler group after data preprocessing.
Optionally, the generating module 302 is further configured to obtain boiler group historical data corresponding to the boiler groups, where the boiler group historical data includes boiler historical data corresponding to each boiler in the boiler groups; processing historical data of the boiler group by using a load prediction algorithm to obtain a load prediction result; and generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data and the load prediction result of the boiler group.
The boiler group history data is data that the boiler group has historically operated, including the energy provided by the boiler group history, i.e., the total load in the boiler group history. The boiler group includes a plurality of boilers. And boiler history data corresponding to each boiler in the boiler group comprises the total load in each boiler history.
Acquiring training data, wherein the training data comprises boiler group historical data corresponding to a plurality of boiler groups; and training the first neural network model by using the training data, so that the first neural network model learns and saves the corresponding relation between the data of the operation of the boiler group and the load prediction result. The load prediction algorithm may be understood as a trained first neural network model.
And generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group and the load prediction result.
Optionally, the generating module 302 is further configured to calculate the boiler characteristics of each boiler in the boiler group by using a characteristic learning algorithm according to the boiler historical data corresponding to each boiler in the boiler group; and generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group, the load prediction result and the boiler characteristics of each boiler in the boiler group.
Acquiring training data, wherein the training data comprises boiler group historical data corresponding to a plurality of boiler groups; and training a second neural network model by using the training data, so that the second neural network model learns and saves the corresponding relation between the operating data of the boiler group and the boiler characteristics of each boiler in the boiler group. The feature learning algorithm may be understood as a trained second neural network model. The first neural network model and the second neural network model may be the same neural network model. The two training sessions above are different mainly for different purposes, or different labels are given to the training data. Before training the neural network model, the training data should be labeled according to the training purpose. The boiler characteristic of a boiler may be properties of the boiler such as the maximum load and the most suitable load of the boiler, etc., and a most suitable load of a boiler is the load at the optimum state of operation of the boiler. The load prediction result is a predicted load that the boiler group needs to bear in a period of time, and the current data of the boiler group comprises the current load of the boiler group. And generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group, the load prediction result and the boiler characteristics of each boiler in the boiler group.
Optionally, the generating module 302 is further configured to obtain boiler group historical data corresponding to the boiler groups, where the boiler group historical data includes boiler historical data corresponding to each boiler in the boiler groups; calculating the boiler characteristics of each boiler in the boiler group by utilizing a characteristic learning algorithm according to the historical data of the boiler corresponding to each boiler in the boiler group; and generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group and the boiler characteristics of each boiler in the boiler group.
The boiler characteristic of a boiler may be an attribute of the boiler, the load prediction result is a predicted load that a group of boilers needs to bear over a period of time, and the current data of the group of boilers includes a current load of the group of boilers. And generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group and the boiler characteristics of each boiler in the boiler group.
Optionally, the generating module 302 is further configured to set an argument of an objective function, wherein the evolutionary algorithm comprises the objective function; acquiring a set function instruction, and generating a constraint condition of an independent variable according to the set function instruction and the boiler characteristics of each boiler in a boiler group; processing historical data of the boiler group by using a load prediction algorithm to obtain a load prediction result; and generating constraint conditions of the target function according to the set function instruction and the load prediction result.
Generating an independent variable constraint condition:
let the argument be x1,x2,...,xnCorresponding boiler characteristic k1,k2,...,kn(ii) a The corresponding load value of the ith boiler is as follows: y isi＝ki*xiThe corresponding standard load is
Wherein the standard load may be understood as the most initial load of the boiler before adjusting the load of the boiler; the maximum lifting load rate is M, and the limited opening time of frequent start and stop is SiThe frequent start-stop limit closing time is PiThe boiler keeps the current state for Ti
The load lifting rate of a single boiler is within a set range:
the state of 1 boiler is changed at most once:
wherein sign is a sign function.
The boiler with the change of start-up and shut-down needs to meet the set time threshold:
generated objective function constraints:
sum of variables O1And (3) minimizing:
total load O of the load of the boiler to be adjusted2Minimum:
the evolution algorithm related parameters also need to be set: according to the number of the boilers, parameters such as the scale of a boiler group, the maximum evolution algebra and the recombination probability are set, and then the optimal solution is obtained through an evolutionary algorithm. The above parameters are common parameters in the evolutionary algorithm, and are not described herein again. The optimal solution may be understood as an optimal solution for scheduling each boiler in the group of boilers.
Optionally, the generating module 302 is further configured to set a value range, an upper and lower boundary, a dimension, and a type of the argument.
The setting may be understood as setting an attribute of an argument, where the attribute of the argument is a common attribute in mathematical computation and is not described herein again.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
Fig. 4 is a schematic diagram of an electronic device 4 provided by the embodiment of the present disclosure. As shown in fig. 4, the electronic apparatus 4 of this embodiment includes: a processor 401, a memory 402 and a computer program 403 stored in the memory 402 and executable on the processor 401. The steps in the various method embodiments described above are implemented when the processor 401 executes the computer program 403. Alternatively, the processor 401 implements the functions of the respective modules/units in the above-described respective apparatus embodiments when executing the computer program 403.
Illustratively, the computer program 403 may be partitioned into one or more modules/units, which are stored in the memory 402 and executed by the processor 401 to accomplish the present disclosure. One or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 403 in the electronic device 4.
The electronic device 4 may be a desktop computer, a notebook, a palm computer, a cloud server, or other electronic devices. The electronic device 4 may include, but is not limited to, a processor 401 and a memory 402. Those skilled in the art will appreciate that fig. 4 is merely an example of the electronic device 4, and does not constitute a limitation of the electronic device 4, and may include more or less components than those shown, or combine certain components, or different components, e.g., the electronic device may also include input-output devices, network access devices, buses, etc.
The Processor 401 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 402 may be an internal storage unit of the electronic device 4, for example, a hard disk or a memory of the electronic device 4. The memory 402 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device 4. Further, the memory 402 may also include both internal storage units of the electronic device 4 and external storage devices. The memory 402 is used for storing computer programs and other programs and data required by the electronic device. The memory 402 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other ways. For example, the above-described apparatus/electronic device embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, and multiple units or components may be combined or integrated into another system, or some features may be omitted or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, the present disclosure may implement all or part of the flow of the method in the above embodiments, and may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the above methods and embodiments. The computer program may comprise computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain suitable additions or additions that may be required in accordance with legislative and patent practices within the jurisdiction, for example, in some jurisdictions, computer readable media may not include electrical carrier signals or telecommunications signals in accordance with legislative and patent practices.
The above examples are only intended to illustrate the technical solutions of the present disclosure, not to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present disclosure, and are intended to be included within the scope of the present disclosure.
Claims (10)
1. A boiler scheduling method, comprising:
acquiring current data of a boiler group corresponding to the boiler group, wherein the current data of the boiler group comprises the current data of a boiler corresponding to each boiler in the boiler group;
generating a boiler scheduling instruction by using an evolutionary algorithm according to the current data of the boiler group;
controlling each boiler in the boiler group by the boiler scheduling command.
2. The method of claim 1, wherein generating boiler scheduling instructions using an evolutionary algorithm based on the current data for the boiler bank comprises:
performing data preprocessing on the current data of the boiler group, wherein the data preprocessing comprises the following steps: deleting interference data in the current data of the boiler group;
and generating the boiler scheduling instruction by utilizing the evolutionary algorithm according to the current data of the boiler group after the data preprocessing.
3. The method of claim 1, wherein generating boiler scheduling instructions using an evolutionary algorithm based on the current data for the boiler bank comprises:
acquiring boiler group historical data corresponding to a boiler group, wherein the boiler group historical data comprises boiler historical data corresponding to each boiler in the boiler group;
processing the historical data of the boiler group by using a load prediction algorithm to obtain a load prediction result;
and generating a boiler scheduling instruction by utilizing the evolutionary algorithm according to the current data of the boiler group and the load prediction result.
4. The method of claim 3, wherein after processing the historical boiler bank data using a load prediction algorithm to obtain a load prediction result, the method further comprises:
calculating the boiler characteristics of each boiler in the boiler group by utilizing a characteristic learning algorithm according to the historical boiler data corresponding to each boiler in the boiler group;
and generating a boiler scheduling instruction by utilizing the evolutionary algorithm according to the current data of the boiler group, the load prediction result and the boiler characteristics of each boiler in the boiler group.
5. The method of claim 1, wherein generating boiler scheduling instructions using an evolutionary algorithm based on the current data for the boiler bank comprises:
acquiring boiler group historical data corresponding to a boiler group, wherein the boiler group historical data comprises boiler historical data corresponding to each boiler in the boiler group;
calculating the boiler characteristics of each boiler in the boiler group by utilizing a characteristic learning algorithm according to the historical boiler data corresponding to each boiler in the boiler group;
and generating a boiler scheduling instruction by utilizing the evolutionary algorithm according to the current data of the boiler group and the boiler characteristics of each boiler in the boiler group.
6. The method of claim 5, wherein after calculating the boiler characteristics of each boiler in the boiler group using a characteristic learning algorithm according to the boiler history data corresponding to each boiler in the boiler group, the method further comprises:
setting an argument of an objective function, wherein the evolutionary algorithm includes the objective function;
acquiring a set function instruction, and generating a constraint condition of the independent variable according to the set function instruction and the boiler characteristics of each boiler in the boiler group;
processing the historical data of the boiler group by using a load prediction algorithm to obtain a load prediction result;
and generating constraint conditions of the target function according to the setting function instruction and the load prediction result.
7. The method of claim 6, wherein setting arguments of the objective function comprises: and setting the value range, the upper boundary, the lower boundary, the dimension and the type of the independent variable.
8. A boiler scheduling apparatus, comprising:
the acquisition module is configured to acquire boiler group current data corresponding to a boiler group, wherein the boiler group current data comprises boiler current data corresponding to each boiler in the boiler group;
the generating module is configured to generate boiler scheduling instructions by using an evolutionary algorithm according to the current data of the boiler group;
a control module configured to control each boiler in the boiler bank by the boiler scheduling instructions.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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