Boiler dispatching method and device

Abstract

The present disclosure relates to the field of artificial intelligence technology, and provides a boiler scheduling method and device. The method includes: obtaining boiler group current data corresponding to the boiler group, wherein the boiler group current data includes boiler current data corresponding to each boiler in the boiler group; generating boiler scheduling instructions using an evolutionary algorithm based on the boiler group current data; and controlling each boiler in the boiler group through the boiler scheduling instructions. The above technical means are used to solve the problem that the automatic scheduling of boilers cannot be realized in the prior art.

Description

Technical Field

[0001] The present disclosure relates to the technical field of boilers, and in particular to a boiler scheduling method and device. Background Art

[0002] With the increasing demand for industrial steam and central heating, it has become a key issue to reasonably dispatch boiler groups in heating stations with multiple steam boilers, so as to minimize energy consumption and maximize efficiency while meeting the steam demand of users. However, there is currently little research on boiler group dispatch optimization in the industry, and it mainly relies on the subjective experience of on-site operators to customize strategies for heating stations, which often fails to achieve the optimal dispatch state.

[0003] In the process of realizing the concept of the present disclosure, the inventors discovered that there are at least the following technical problems in the related art: the problem of being unable to realize automatic scheduling of the boiler. Summary of the invention

[0004] In view of this, the embodiments of the present disclosure provide a boiler scheduling method, device, electronic device and computer-readable storage medium to solve the problem in the prior art that automatic scheduling of boilers cannot be achieved.

[0005] According to a first aspect of an embodiment of the present disclosure, a boiler scheduling method is provided, comprising: obtaining boiler group current data corresponding to the boiler group, wherein the boiler group current data includes boiler current data corresponding to each boiler in the boiler group; generating a boiler scheduling instruction using an evolutionary algorithm based on the boiler group current data; and controlling each boiler in the boiler group through the boiler scheduling instruction.

[0006] According to a second aspect of an embodiment of the present disclosure, a boiler scheduling device is provided, comprising: an acquisition module, configured to acquire boiler group current data corresponding to the boiler group, wherein the boiler group current data includes boiler current data corresponding to each boiler in the boiler group; a generation module, configured to generate boiler scheduling instructions using an evolutionary algorithm based on the boiler group current data; and a control module, configured to control each boiler in the boiler group through the boiler scheduling instructions.

[0007] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0008] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, which stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0009] Compared with the prior art, the disclosed embodiment has the following beneficial effects: obtaining the current data of the boiler group corresponding to the boiler group, wherein the current data of the boiler group includes the current data of the boiler corresponding to each boiler in the boiler group; generating boiler scheduling instructions using an evolutionary algorithm based on the current data of the boiler group; and controlling each boiler in the boiler group through the boiler scheduling instructions. The above technical means can solve the problem that the automatic scheduling of boilers cannot be realized in the prior art, thereby providing a method for automatic scheduling of boilers. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0011] Figure 1 is a schematic diagram of an application scenario of an embodiment of the present disclosure;

[0012] Figure 2 is a flow chart of a boiler scheduling method provided by an embodiment of the present disclosure;

[0013] Figure 3 is a structural schematic diagram of a boiler scheduling device provided by an embodiment of the present disclosure;

[0014] Figure 4 It is a structural schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0015] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0016] A boiler scheduling method and device according to an embodiment of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0017] Figure 1 1 is a schematic diagram of an application scenario of an embodiment of the present disclosure. The application scenario may include terminal devices 1, 2 and 3, a server 4 and a network 5.

[0018] Terminal devices 1, 2 and 3 can be hardware or software. When terminal devices 1, 2 and 3 are hardware, they can be various electronic devices with display screens and supporting communication with server 4, including but not limited to smart phones, tablet computers, laptop portable computers and desktop computers, etc.; when terminal devices 1, 2 and 3 are software, they can be installed in the above electronic devices. Terminal devices 1, 2 and 3 can be implemented as multiple software or software modules, or as a single software or software module, and the embodiments of the present disclosure are not limited to this. Furthermore, various applications can be installed on terminal devices 1, 2 and 3, such as data processing applications, instant messaging tools, social platform software, search applications, shopping applications, etc.

[0019] The server 4 may be a server that provides various services, for example, a background server that receives a request sent by a terminal device that establishes a communication connection with the server, and the background server may receive and analyze the request sent by the terminal device, and generate a processing result. The server 4 may be a single server, or a server cluster composed of several servers, or a cloud computing service center, which is not limited in the embodiments of the present disclosure.

[0020] It should be noted that the server 4 can be hardware or software. When the server 4 is hardware, it can be various electronic devices that provide various services for the terminal devices 1, 2, and 3. When the server 4 is software, it can be multiple software or software modules that provide various services for the terminal devices 1, 2, and 3, or it can be a single software or software module that provides various services for the terminal devices 1, 2, and 3, and the embodiments of the present disclosure are not limited to this.

[0021] The network 5 can be a wired network connected by coaxial cable, twisted pair and optical fiber, or it can be a wireless network that can interconnect various communication devices without wiring, such as Bluetooth, Near Field Communication (NFC), infrared, etc., which is not limited in the embodiments of the present disclosure.

[0022] The 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 send information, etc. It should be noted that the specific types, quantities and combinations of the terminal devices 1, 2 and 3, the server 4 and the network 5 can be adjusted according to the actual needs of the application scenario, and the embodiments of the present disclosure are not limited to this.

[0023] Figure 2 It is a flow chart of a boiler scheduling method provided in an embodiment of the present disclosure. Figure 2 The boiler scheduling method can be obtained by Figure 1 The terminal device or server executes. Figure 2 As shown, the boiler scheduling method includes:

[0024] S201, obtaining boiler group current data corresponding to the boiler group, wherein the boiler group current data includes boiler current data corresponding to each boiler in the boiler group;

[0025] S202, generating boiler dispatching instructions using an evolutionary algorithm based on current data of the boiler group;

[0026] S203, controlling each boiler in the boiler group through boiler scheduling instructions.

[0027] The current data of the boiler group is the data of the current operation of the boiler group, including the energy currently provided by the boiler group, that is, the current load of the boiler group. The boiler group includes multiple boilers. The current data of the boiler corresponding to each boiler in the boiler group includes the current load of each boiler. The boiler scheduling instruction is used to adjust the load of each boiler in the boiler group. For example, the boiler group includes three boilers A, B and C. The load of boiler A is the largest and the load of boiler C is the smallest. For safety reasons, the load of boiler A can be reduced and the load of boiler C can be increased.

[0028] Evolutionary algorithms, or "evolutionary algorithms" (EAs), are a "family of algorithms". Although they have many variations, with different genetic gene expressions, different crossover and mutation operators, references to special operators, and different regeneration and selection methods, they are all inspired by biological evolution in nature. Compared with traditional optimization algorithms such as calculus-based methods and exhaustive methods, evolutionary computation is a mature global optimization method with high robustness and wide applicability. It has the characteristics of self-organization, self-adaptation, and self-learning. It can effectively handle complex problems that are difficult to solve with traditional optimization algorithms without being restricted by the nature of the problem.

[0029] According to the technical solution provided by the embodiment of the present disclosure, the current data of the boiler group corresponding to the boiler group is obtained, wherein the current data of the boiler group includes the current data of the boiler corresponding to each boiler in the boiler group; according to the current data of the boiler group, the boiler scheduling instruction is generated by using the evolutionary algorithm; each boiler in the boiler group is controlled by the boiler scheduling instruction. The above technical means can solve the problem that the automatic scheduling of the boiler cannot be realized in the prior art, and thus provide a method for automatic scheduling of the boiler.

[0030] In step S202, based on the current data of the boiler group, an evolutionary algorithm is used to generate a boiler scheduling instruction, including: performing 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; based on the current data of the boiler group after data preprocessing, an evolutionary algorithm is used to generate a boiler scheduling instruction.

[0031] Because the boiler still has steam flow for a period of time after being shut down, the interference data can be the load data corresponding to the steam flow after the boiler is shut down. Through the current data of the boiler group after data preprocessing, more accurate boiler scheduling instructions can be generated.

[0032] In step S202, an evolutionary algorithm is used to generate a boiler scheduling instruction based on the current data of the boiler group, including: obtaining boiler group historical data corresponding to the boiler group, wherein the boiler group historical data includes boiler historical data corresponding to each boiler in the boiler group; processing the boiler group historical data using a load prediction algorithm to obtain a load prediction result; and generating a boiler scheduling instruction based on the current data of the boiler group and the load prediction result using an evolutionary algorithm.

[0033] The boiler group historical data is the data of the boiler group's historical operation, including the energy provided by the boiler group in history, that is, the total load in the boiler group's history. The boiler group includes multiple boilers. The boiler historical data corresponding to each boiler in the boiler group includes the total load in each boiler's history.

[0034] Acquire training data, wherein the training data includes boiler group historical data corresponding to multiple boiler groups; use the training data to train a first neural network model, so that the first neural network model learns and saves the corresponding relationship between the operation data of the boiler group and the load forecasting result. The load forecasting algorithm can be understood as the trained first neural network model.

[0035] The load forecast result is the load that the boiler group needs to bear within a predicted period of time. The current data of the boiler group includes the current load of the boiler group. According to the current data of the boiler group and the load forecast result, the evolutionary algorithm is used to generate the boiler scheduling instruction.

[0036] In step S202, after the load prediction result is obtained by processing the historical data of the boiler group using the load prediction algorithm, the method further includes: calculating the boiler characteristics of each boiler in the boiler group using a feature learning algorithm based on the boiler historical data corresponding to each boiler in the boiler group; and generating boiler scheduling instructions using an evolutionary algorithm based on the current data of the boiler group, the load prediction result and the boiler characteristics of each boiler in the boiler group.

[0037] Acquire training data, wherein the training data includes historical data of boiler groups corresponding to multiple boiler groups; train the second neural network model using the training data so that the second neural network model learns and saves the corresponding relationship between the operation data of the boiler group and the boiler characteristics of each boiler in the boiler group. The feature learning algorithm can be understood as the trained second neural network model. The first neural network model and the second neural network model can be the same neural network model. The two trainings mentioned above are mainly due to different training purposes or different annotations of the training data. Before training the neural network model with the training data, the training data should be annotated according to the training purpose. The boiler characteristics of a boiler can be the attributes of the boiler, such as the maximum load and the most suitable load of the boiler, etc. The most suitable load of a boiler is the load under the best operating state of the boiler. The load prediction result is the load that the boiler group needs to bear within a period of time, and the current data of the boiler group includes the current load of the boiler group. According to the current data of the boiler group, the load prediction result and the boiler characteristics of each boiler in the boiler group, the evolutionary algorithm is used to generate the boiler scheduling instruction.

[0038] In step S202, an evolutionary algorithm is used to generate a boiler scheduling instruction based on the current data of the boiler group, including: obtaining boiler group historical data corresponding to the boiler group, wherein the boiler group historical data includes boiler historical data corresponding to each boiler in the boiler group; calculating the boiler characteristics of each boiler in the boiler group using a feature learning algorithm based on the boiler historical data corresponding to each boiler in the boiler group; and generating a boiler scheduling instruction using an evolutionary algorithm based on the current data of the boiler group and the boiler characteristics of each boiler in the boiler group.

[0039] The boiler characteristics of a boiler can be the attributes of the boiler. The load forecast result is the load that the boiler group needs to bear within a period of time. The current data of the boiler group includes the current load of the boiler group. Based on the current data of the boiler group and the boiler characteristics of each boiler in the boiler group, the boiler scheduling instruction is generated using the evolutionary algorithm.

[0040] After calculating the boiler characteristics of each boiler in the boiler group using a feature learning algorithm based on the boiler historical data corresponding to each boiler in the boiler group, the method also includes: setting the independent variables of the objective function, wherein the evolutionary algorithm includes the objective function; obtaining a setting function instruction, and generating constraints on the independent variables based on the setting function instruction and the boiler characteristics of each boiler in the boiler group; processing the boiler group historical data using a load prediction algorithm to obtain a load prediction result; and generating constraints on the objective function based on the setting function instruction and the load prediction result.

[0041] Generate independent variable constraints:

[0042] Let the independent variables be x1, x2, ..., x n, the corresponding boiler characteristics are k1, k2, ..., k n ; Then the load value corresponding to the i-th boiler is: Y i =k i *x i , the corresponding standard load is The standard load can be understood as the initial load of the boiler before adjusting the load of the boiler; the maximum load increase and decrease rate is M, and the frequent start and stop limit opening time is S. i , the frequent start-stop limit closing time is P i The boiler maintains the current state for T i

[0043] The load rate of a single boiler is within the set range:

[0044]

[0045] Set to change the status of only one boiler at a time:

[0046]

[0047] Among them, sign is the sign function.

[0048] The boiler that starts and stops the boiler needs to meet the set time threshold:

[0049]

[0050] The generated objective function constraints are:

[0051] The sum of variables O1 is minimized:

[0052]

[0053] The total load O2 of the boiler that needs to be adjusted is the minimum:

[0054]

[0055] It is also necessary to set the parameters related to the evolutionary algorithm: according to the number of boilers, set the boiler group scale, maximum evolutionary generations, recombination probability and other parameters, and then use the evolutionary algorithm to find the optimal solution. The above parameters are commonly used parameters in the evolutionary algorithm and will not be repeated here. The optimal solution can be understood as the optimal solution for scheduling each boiler in the boiler group.

[0056] Set the independent variables of the objective function, including: setting the value range, upper and lower boundaries, dimension and type of the independent variables.

[0057] The above settings can be understood as setting the properties of the independent variables. The properties of the above independent variables are commonly used in mathematical calculations and will not be described in detail here.

[0058] All the above optional technical solutions can be arbitrarily combined to form optional embodiments of the present application, which will not be described one by one here.

[0059] The following are embodiments of the device disclosed herein, which can be used to execute the method embodiments disclosed herein. For details not disclosed in the device embodiments disclosed herein, please refer to the method embodiments disclosed herein.

[0060] Figure 3 Schematic diagram of a boiler scheduling device provided by an embodiment of the present disclosure. Figure 3 As shown, the boiler scheduling device includes:

[0061] The acquisition module 301 is configured to acquire boiler group current data corresponding to the boiler group, wherein the boiler group current data includes boiler current data corresponding to each boiler in the boiler group;

[0062] A generation module 302 is configured to generate boiler scheduling instructions using an evolutionary algorithm based on current data of the boiler group;

[0063] The control module 303 is configured to control each boiler in the boiler group through boiler scheduling instructions.

[0064] The current data of the boiler group is the data of the current operation of the boiler group, including the energy currently provided by the boiler group, that is, the current load of the boiler group. The boiler group includes multiple boilers. The current data of the boiler corresponding to each boiler in the boiler group includes the current load of each boiler. The boiler scheduling instruction is used to adjust the load of each boiler in the boiler group. For example, the boiler group includes three boilers A, B and C. The load of boiler A is the largest and the load of boiler C is the smallest. For safety reasons, the load of boiler A can be reduced and the load of boiler C can be increased.

[0065] Evolutionary algorithms, or "evolutionary algorithms" (EAs), are a "family of algorithms". Although they have many variations, with different genetic gene expressions, different crossover and mutation operators, references to special operators, and different regeneration and selection methods, they are all inspired by biological evolution in nature. Compared with traditional optimization algorithms such as calculus-based methods and exhaustive methods, evolutionary computation is a mature global optimization method with high robustness and wide applicability. It has the characteristics of self-organization, self-adaptation, and self-learning. It can effectively handle complex problems that are difficult to solve with traditional optimization algorithms without being restricted by the nature of the problem.

[0066] According to the technical solution provided by the embodiment of the present disclosure, the current data of the boiler group corresponding to the boiler group is obtained, wherein the current data of the boiler group includes the current data of the boiler corresponding to each boiler in the boiler group; according to the current data of the boiler group, the boiler scheduling instruction is generated by using the evolutionary algorithm; each boiler in the boiler group is controlled by the boiler scheduling instruction. The above technical means can solve the problem that the automatic scheduling of the boiler cannot be realized in the prior art, and thus provide a method for automatic scheduling of the boiler.

[0067] Optionally, the generation 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 boiler scheduling instructions using an evolutionary algorithm based on the current data of the boiler group after data preprocessing.

[0068] Because the boiler still has steam flow for a period of time after being shut down, the interference data can be the load data corresponding to the steam flow after the boiler is shut down. Through the current data of the boiler group after data preprocessing, more accurate boiler scheduling instructions can be generated.

[0069] Optionally, the generation module 302 is also configured to obtain boiler group historical data corresponding to the boiler group, wherein the boiler group historical data includes boiler historical data corresponding to each boiler in the boiler group; process the boiler group historical data using a load prediction algorithm to obtain a load prediction result; and generate a boiler scheduling instruction using an evolutionary algorithm based on the boiler group current data and the load prediction result.

[0070] The boiler group historical data is the data of the boiler group's historical operation, including the energy provided by the boiler group in history, that is, the total load in the boiler group's history. The boiler group includes multiple boilers. The boiler historical data corresponding to each boiler in the boiler group includes the total load in each boiler's history.

[0071] Acquire training data, wherein the training data includes boiler group historical data corresponding to multiple boiler groups; use the training data to train a first neural network model, so that the first neural network model learns and saves the corresponding relationship between the operation data of the boiler group and the load forecasting result. The load forecasting algorithm can be understood as the trained first neural network model.

[0072] The load forecast result is the load that the boiler group needs to bear within a predicted period of time. The current data of the boiler group includes the current load of the boiler group. According to the current data of the boiler group and the load forecast result, the evolutionary algorithm is used to generate the boiler scheduling instruction.

[0073] Optionally, the generation module 302 is also configured to calculate the boiler characteristics of each boiler in the boiler group using a feature learning algorithm based on the boiler historical data corresponding to each boiler in the boiler group; and generate boiler scheduling instructions using an evolutionary algorithm based on the current data of the boiler group, load forecast results and the boiler characteristics of each boiler in the boiler group.

[0074] Acquire training data, wherein the training data includes historical data of boiler groups corresponding to multiple boiler groups; train the second neural network model using the training data so that the second neural network model learns and saves the corresponding relationship between the operation data of the boiler group and the boiler characteristics of each boiler in the boiler group. The feature learning algorithm can be understood as the trained second neural network model. The first neural network model and the second neural network model can be the same neural network model. The two trainings mentioned above are mainly due to different training purposes or different annotations of the training data. Before training the neural network model with the training data, the training data should be annotated according to the training purpose. The boiler characteristics of a boiler can be the attributes of the boiler, such as the maximum load and the most suitable load of the boiler, etc. The most suitable load of a boiler is the load under the best operating state of the boiler. The load prediction result is the load that the boiler group needs to bear within a period of time, and the current data of the boiler group includes the current load of the boiler group. According to the current data of the boiler group, the load prediction result and the boiler characteristics of each boiler in the boiler group, the evolutionary algorithm is used to generate the boiler scheduling instruction.

[0075] Optionally, the generation module 302 is also configured to obtain boiler group historical data corresponding to the boiler group, wherein the boiler group historical data includes boiler historical data corresponding to each boiler in the boiler group; based on the boiler historical data corresponding to each boiler in the boiler group, calculate the boiler characteristics of each boiler in the boiler group using a feature learning algorithm; and generate boiler scheduling instructions using an evolutionary algorithm based on the boiler group current data and the boiler characteristics of each boiler in the boiler group.

[0076] The boiler characteristics of a boiler can be the attributes of the boiler. The load forecast result is the load that the boiler group needs to bear within a period of time. The current data of the boiler group includes the current load of the boiler group. Based on the current data of the boiler group and the boiler characteristics of each boiler in the boiler group, the boiler scheduling instruction is generated using the evolutionary algorithm.

[0077] Optionally, the generation module 302 is also configured to set the independent variables of the objective function, wherein the evolutionary algorithm includes the objective function; obtain the setting function instruction, and generate the constraint conditions of the independent variables based on the setting function instruction and the boiler characteristics of each boiler in the boiler group; use the load prediction algorithm to process the boiler group historical data to obtain the load prediction result; generate the constraint conditions of the objective function based on the setting function instruction and the load prediction result.

[0078] Generate independent variable constraints:

[0079] Let the independent variables be x1, x2, ..., x n , the corresponding boiler characteristics are k1, k2, ..., k n ; Then the load value corresponding to the i-th boiler is: Y i =k i *x i , the corresponding standard load is The standard load can be understood as the initial load of the boiler before adjusting the load of the boiler; the maximum load increase and decrease rate is M, and the frequent start and stop limit opening time is S. i , the frequent start-stop limit closing time is P i The boiler maintains the current state for T i

[0080] The load rate of a single boiler is within the set range:

[0081]

[0082] Set to change the status of only one boiler at a time:

[0083]

[0084] Among them, sign is the sign function.

[0085] The boiler that starts and stops the boiler needs to meet the set time threshold:

[0086]

[0087] The generated objective function constraints are:

[0088] The sum of variables O1 is minimized:

[0089]

[0090] The total load O2 of the boiler that needs to be adjusted is the minimum:

[0091]

[0092] It is also necessary to set the parameters related to the evolutionary algorithm: according to the number of boilers, set the boiler group scale, maximum evolutionary generations, recombination probability and other parameters, and then use the evolutionary algorithm to find the optimal solution. The above parameters are commonly used parameters in the evolutionary algorithm and will not be repeated here. The optimal solution can be understood as the optimal solution for scheduling each boiler in the boiler group.

[0093] Optionally, the generation module 302 is further configured to set the value range, upper and lower boundaries, dimension and type of the independent variable.

[0094] The above settings can be understood as setting the properties of the independent variables. The properties of the above independent variables are commonly used in mathematical calculations and will not be described in detail here.

[0095] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.

[0096] Figure 4 Schematic diagram of an electronic device 4 provided in an embodiment of the present disclosure. Figure 4 As shown, the electronic device 4 of this embodiment includes: a processor 401, a memory 402, and a computer program 403 stored in the memory 402 and executable on the processor 401. When the processor 401 executes the computer program 403, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor 401 executes the computer program 403, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0097] Exemplarily, the computer program 403 may be divided into one or more modules / units, which are stored in the memory 402 and executed by the processor 401 to complete the present disclosure. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 403 in the electronic device 4.

[0098] The electronic device 4 may be a desktop computer, a notebook, a PDA, a cloud server, or other electronic device. 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 Figure 4 It is only an example of the electronic device 4 and does not constitute a limitation of the electronic device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0099] The processor 401 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0100] The memory 402 may be an internal storage unit of the electronic device 4, for example, a hard disk or memory of the electronic device 4. The memory 402 may also be an external storage device of the electronic device 4, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 4. Further, the memory 402 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 402 is used to store 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.

[0101] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0102] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0103] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0104] In the embodiments provided in the present disclosure, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. Multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0105] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0107] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present disclosure implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. The computer program may include computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0108] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should all be included in the protection scope of the present disclosure.

Claims

1. A boiler scheduling method, characterized in that: include: Acquire boiler group current data corresponding to the boiler group, wherein the boiler group current data includes boiler current data corresponding to each boiler in the boiler group; Generate boiler dispatching instructions using an evolutionary algorithm based on the current data of the boiler group; Control each boiler in the boiler group by means of the boiler scheduling instruction; The method of generating a boiler dispatch instruction by using an evolutionary algorithm according to the current data of the boiler group includes: obtaining boiler group historical data corresponding to the boiler group, wherein the boiler group historical data includes boiler historical data corresponding to each boiler in the boiler group; calculating the boiler characteristics of each boiler in the boiler group by using a feature learning algorithm according to the boiler historical data corresponding to each boiler in the boiler group; generating a boiler dispatch instruction by using the evolutionary algorithm according to the current data of the boiler group and the boiler characteristics of each boiler in the boiler group; Wherein, after calculating the boiler characteristics of each boiler in the boiler group using a feature learning algorithm based on the boiler historical data corresponding to each boiler in the boiler group, the method further includes: setting the independent variable of the objective function, wherein the evolutionary algorithm includes the objective function; obtaining a setting function instruction, and generating constraints of the independent variable based on the setting function instruction and the boiler characteristics of each boiler in the boiler group; processing the boiler group historical data using a load prediction algorithm to obtain a load prediction result; generating constraints of the objective function based on the setting function instruction and the load prediction result; Generate the constraints: Let the independent variables be x1, x2, ..., x n , the corresponding boiler characteristics are k1, k2, ..., k n ; Then the load value corresponding to the i-th boiler is: Y i =k i *x i , the corresponding standard load is The standard load can be understood as the initial load of the boiler before adjusting the load of the boiler; the maximum load increase and decrease rate is M, and the frequent start and stop limit opening time is S. i , the frequent start-stop limit closing time is P i The boiler maintains the current state for T i ; The load rate of a single boiler is within the set range: Set to change the status of only one boiler at a time: Among them, sign is the sign function; The boiler that starts and stops the boiler needs to meet the set time threshold: The generated objective function constraints are: The sum of variables O1 is minimized: The total load O2 of the boiler that needs to be adjusted is the minimum:

2. The method according to claim 1, characterized in that: The step of generating boiler dispatching instructions by using an evolutionary algorithm according to the current data of the boiler group includes: Performing 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; The boiler scheduling instruction is generated by using the evolutionary algorithm according to the current data of the boiler group after the data preprocessing.

3. The method according to claim 1, characterized in that The step of generating boiler dispatching instructions by using an evolutionary algorithm according to the current data of the boiler group includes: Acquire boiler group historical data corresponding to the boiler group, wherein the boiler group historical data includes boiler historical data corresponding to each boiler in the boiler group; Processing the boiler group historical data using a load forecasting algorithm to obtain a load forecasting result; The evolutionary algorithm is used to generate boiler scheduling instructions according to the current data of the boiler group and the load forecast result.

4. The method according to claim 3, characterized in that After the load prediction algorithm is used to process the boiler group historical data to obtain the load prediction result, the method further includes: Calculating boiler features of each boiler in the boiler group using a feature learning algorithm according to boiler historical data corresponding to each boiler in the boiler group; The boiler scheduling instruction is generated by using 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 according to claim 1, characterized in that The setting of the independent variable of the objective function includes: setting the value range, upper and lower boundaries, dimension and type of the independent variable.

6. A boiler dispatching device, characterized in that: include: An acquisition module is configured to acquire boiler group current data corresponding to the boiler group, wherein the boiler group current data includes boiler current data corresponding to each boiler in the boiler group; A generation module is configured to generate boiler scheduling instructions using an evolutionary algorithm based on current data of the boiler group; A control module, configured to control each boiler in the boiler group through the boiler scheduling instruction; The generation module is further configured to obtain boiler group historical data corresponding to the boiler group, wherein the boiler group historical data includes boiler historical data corresponding to each boiler in the boiler group; calculate the boiler characteristics of each boiler in the boiler group using a feature learning algorithm according to the boiler historical data corresponding to each boiler in the boiler group; generate a boiler scheduling instruction using the evolutionary algorithm according to the boiler group current data and the boiler characteristics of each boiler in the boiler group; The generation module is further configured to set an independent variable of an objective function, wherein the evolutionary algorithm includes the objective function; obtain a setting function instruction, and generate constraints of the independent variable according to the setting function instruction and boiler characteristics of each boiler in the boiler group; process the boiler group historical data using a load prediction algorithm to obtain a load prediction result; generate constraints of the objective function according to the setting function instruction and the load prediction result; Generate the constraints: Let the independent variables be x1, x2, ..., x n , the corresponding boiler characteristics are k1, k2, ..., k n ; Then the load value corresponding to the i-th boiler is: Y i =k i *x i , the corresponding standard load is The standard load can be understood as the initial load of the boiler before adjusting the load of the boiler; the maximum load increase and decrease rate is M, and the frequent start and stop limit opening time is S. i , the frequent start-stop limit closing time is P i The boiler maintains the current state for T i ; The load rate of a single boiler is within the set range: Set to change the status of only one boiler at a time: Among them, sign is the sign function; The boiler that starts and stops the boiler needs to meet the set time threshold: The generated objective function constraints are: The sum of variables O1 is minimized: The total load O2 of the boiler that needs to be adjusted is the minimum:

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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