CN117114203A - Nuclear energy heating load prediction method and device and electronic equipment - Google Patents
Nuclear energy heating load prediction method and device and electronic equipment Download PDFInfo
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Abstract
The application provides a method and a device for predicting nuclear energy heating load and electronic equipment. The method comprises the following steps: acquiring a plurality of groups of nuclear energy heat supply data in a historical time period; each group of nuclear energy heat supply data comprises nuclear energy heat supply operation data and heat supply load values under corresponding sampling time; determining n target parameters based on the plurality of groups of nuclear energy heating data; wherein n is an integer greater than or equal to 2; acquiring parameter values of each target parameter; and obtaining the predicted value of the nuclear energy heat supply load according to the parameter values of the n target parameters and the multiple groups of nuclear energy heat supply data. The scheme can realize the prediction of the nuclear energy heating load based on the history nuclear energy heating data.
Description
Technical Field
The application relates to the technical field of nuclear power heat supply, in particular to a method and a device for predicting nuclear energy heat supply load and electronic equipment.
Background
Nuclear energy is one of the important props for meeting energy supply and ensuring national safety. Compared with clean energy sources such as photovoltaic, wind power, water and electricity, the nuclear power has the advantages of no intermittence, less constraint by natural conditions and the like, and can replace fossil energy sources on a large scale.
With the development of nuclear reactor safety technology, nuclear energy is used as clean and efficient clean energy, and has unique superiority in the aspects of central heating and industrial steam supply and huge application potential. The nuclear energy heat supply belongs to a zero-carbon heat supply mode, in order to promote the efficient utilization of nuclear energy, the prediction of the nuclear energy heat supply load needs to be accurately carried out, and an important basis is provided for the operation strategy adjustment of a nuclear energy heat supply unit so as to ensure the safe and reliable unit operation and external heat supply.
Disclosure of Invention
In order to solve the problems, the application provides a method and a device for predicting a nuclear energy heating load and electronic equipment.
According to a first aspect of the present application there is provided a method of predicting a nuclear heating load comprising:
acquiring a plurality of groups of nuclear energy heat supply data in a historical time period; each group of nuclear energy heat supply data comprises nuclear energy heat supply operation data and heat supply load values under corresponding sampling time;
determining n target parameters based on the plurality of groups of nuclear energy heating data; wherein n is an integer greater than or equal to 2;
acquiring a parameter value of each target parameter;
and obtaining the predicted value of the nuclear energy heat supply load according to the parameter values of the n target parameters and the multiple groups of nuclear energy heat supply data.
As a possible implementation manner, the nuclear energy heating operation data includes at least one of the following: resident side heat supply data, operation condition data of the secondary station, meteorological data, operation state data of the heat supply first station and design data of the heat supply pipeline.
In some embodiments of the application, the determining n target parameters based on the plurality of sets of nuclear heating data includes:
determining weight values of the influence of each parameter on heat supply based on causal relations among the parameters in the plurality of groups of nuclear energy heat supply data;
and determining the n target parameters according to the weight values of the parameters on the heat supply.
As an example, the determining the n target parameters according to the weight values of the respective parameters on the heating effect includes:
sequencing the parameters from big to small according to the weight values;
and determining the first n parameters in the sequencing result as the n target parameters.
In some embodiments of the present application, the obtaining the predicted value of the nuclear heating load according to the parameter values of the n target parameters and the plurality of sets of nuclear heating data includes:
determining a set of target nuclear energy heat supply data matched with the parameter values of the n target parameters from the plurality of sets of nuclear energy heat supply data;
and determining a predicted value of the nuclear energy heating load according to the target nuclear energy heating data.
As a possible implementation manner, the determining, from the multiple sets of nuclear energy heating data, a set of target nuclear energy heating data matched with parameter values of the n target parameters includes:
for each group of nuclear energy heat supply data, calculating Euclidean distances between the nuclear energy heat supply data and parameter values of the n target parameters;
and determining a group of nuclear energy heat supply data with the minimum Euclidean distance between the parameter values of the n target parameters as the target nuclear energy heat supply data.
Wherein the determining the predicted value of the nuclear heating load according to the target nuclear heating data comprises:
and determining the heat supply load value in the target nuclear energy heat supply data as the predicted value of the nuclear energy heat supply load.
According to a second aspect of the present application, there is provided a nuclear heating load prediction apparatus comprising:
the first acquisition module is used for acquiring a plurality of groups of nuclear energy heat supply data in a historical time period; each group of nuclear energy heat supply data comprises nuclear energy heat supply operation data and heat supply load values under corresponding sampling time;
the determining module is used for determining n target parameters based on the plurality of groups of nuclear energy heat supply data; wherein n is an integer greater than or equal to 2;
the second acquisition module is used for acquiring the parameter value of each target parameter;
and the third acquisition module is used for acquiring the predicted value of the nuclear energy heat supply load according to the parameter values of the n target parameters and the plurality of groups of nuclear energy heat supply data.
Wherein the nuclear heating operation data comprises at least one of: resident side heat supply data, operation condition data of the secondary station, meteorological data, operation state data of the heat supply first station and design data of the heat supply pipeline.
In some embodiments of the present application, the determining module is specifically configured to:
determining weight values of the influence of each parameter on heat supply based on causal relations among the parameters in the plurality of groups of nuclear energy heat supply data;
and determining the n target parameters according to the weight values of the parameters on the heat supply.
As a possible implementation, the determining module is further configured to:
sequencing the parameters from big to small according to the weight values;
and determining the first n parameters in the sequencing result as the n target parameters.
In some embodiments of the application, the third acquisition module comprises:
a first determining unit configured to determine, from the plurality of sets of nuclear power heating data, a set of target nuclear power heating data that matches parameter values of the n target parameters;
and the second determining unit is used for determining the predicted value of the nuclear energy heat supply load according to the target nuclear energy heat supply data.
As a possible implementation manner, the first determining unit is specifically configured to:
for each group of nuclear energy heat supply data, calculating Euclidean distances between the nuclear energy heat supply data and parameter values of the n target parameters;
and determining a group of nuclear energy heat supply data with the minimum Euclidean distance between the parameter values of the n target parameters as the target nuclear energy heat supply data.
As a possible implementation manner, the second determining unit is specifically configured to:
and determining the heat supply load value in the target nuclear energy heat supply data as the predicted value of the nuclear energy heat supply load.
According to a third aspect of the present application there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of the first aspect described above when executing the program.
According to a fourth aspect of the present application there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of the first aspect described above.
According to the technical scheme of the application, a plurality of groups of nuclear energy heat supply data in a historical time period are obtained, n target parameters are determined based on the plurality of groups of nuclear energy heat supply data, the parameter value of each target parameter is obtained, and the predicted value of the nuclear energy heat supply load is obtained according to the parameter value of each target parameter and the plurality of groups of nuclear energy heat supply data. According to the scheme, the prediction of the nuclear energy heat supply load based on the history nuclear energy heat supply data can be realized, so that the operation strategy of the nuclear energy heat supply unit can be adjusted based on the nuclear energy load predicted value, the efficient utilization of nuclear energy can be improved, and the unit operation and the safety and reliability of external heat supply can be ensured.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a method for predicting nuclear heating load according to an embodiment of the present application;
FIG. 2 is a flow chart of another method for predicting nuclear heating load according to an embodiment of the present application;
FIG. 3 is a block diagram of a prediction apparatus for nuclear heating load according to an embodiment of the present application;
fig. 4 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application.
It should be noted that nuclear energy is one of important props for satisfying energy supply and ensuring national security. Compared with clean energy sources such as photovoltaic, wind power, water and electricity, the nuclear power has the advantages of no intermittence, less constraint by natural conditions and the like, and can replace fossil energy sources on a large scale.
With the development of nuclear reactor safety technology, nuclear energy is used as clean and efficient clean energy, and has unique superiority in the aspects of central heating and industrial steam supply and huge application potential. The nuclear energy heat supply belongs to a zero-carbon heat supply mode, in order to promote the efficient utilization of nuclear energy, the prediction of the nuclear energy heat supply load needs to be accurately carried out, and an important basis is provided for the operation strategy adjustment of a nuclear energy heat supply unit so as to ensure the safe and reliable unit operation and external heat supply.
Because nuclear heat supply is a new heat supply mode, work based on nuclear heat supply load prediction is not developed at present, and the load prediction directly using thermal power heat supply has the following problems: (1) The nuclear power plant does not participate in peak regulation, almost keeps full-output operation, directly influences the consumption of nuclear fuel due to uncontrollable electric load and external heat supply, and can not meet the actual requirements of the nuclear power unit because the heat supply load is predicted according to a thermal power heat supply mode; (2) The nuclear energy heat supply is directly merged into a municipal heat supply pipe network after being heated from a heat supply first station of a power plant, and the heat supply of a final user is finished by a heat supply operation unit, so that the operation mode of direct heat supply to a user side through a secondary heat supply network is basically different; (3) The nuclear power plant does not directly monitor the direct user and the secondary heat supply network, and once the secondary heat supply network is abnormal, nuclear power operation monitoring disc personnel are not timely operated, the overpressure of a heat supply first station is easily caused, and the influence on safety is caused.
In order to solve the problems, the application provides a method and a device for predicting a nuclear energy heating load and electronic equipment.
Fig. 1 is a flowchart of a method for predicting a nuclear heating load according to an embodiment of the present application. It should be noted that, the method for predicting the nuclear heating load according to the embodiment of the present application may be applied to the device for predicting the nuclear heating load according to the embodiment of the present application, and the device may be configured in an electronic device. As shown in fig. 1, the method may include:
step 101, acquiring a plurality of groups of nuclear energy heat supply data in a historical time period; each group of nuclear energy heat supply data comprises nuclear energy heat supply operation data and heat supply load values under corresponding sampling time.
Wherein the historical time period may be a period of time in a past heating cycle, such as a month in the heating cycle. Each set of nuclear heating data includes nuclear heating operation data at a corresponding sampling time and a heating load value at a corresponding sampling time. As an example, data sampling may be performed every 1 hour, each sampling obtaining a set of nuclear heating data.
As one possible implementation, the nuclear heating operation data in each set of nuclear heating data may include at least one of: resident side heat supply data, operation condition data of the secondary station, meteorological data, operation state data of the heat supply first station and design data of the heat supply pipeline. The resident side heat supply data can comprise parameter information such as resident side room temperature, resident side heat supply area and the like, for example, the resident side room temperature can be obtained through an equipped temperature sensor, and the resident side heat supply area is determined through the building area of a resident corresponding residence with heat supply being conducted. Wherein the resident side room temperature may in turn comprise the lowest room temperature, the highest room temperature, the average of the room temperatures, etc.
The operation condition data of the secondary station can comprise parameter information such as temperature, pressure and flow of backwater, the data can be obtained based on a corresponding monitoring system, and the data can be obtained after filling and submitting the data through an interactable page in the terminal equipment based on related staff. The weather data may be weather data of a corresponding heating area, such as weather data of a nuclear power plant, which may be obtained by a weather monitoring station built in the nuclear power plant, or a weather bureau database of the area. The operation condition data of the heat supply first station can comprise parameter information such as valve opening, temperature, pressure, flow and the like of the heat source, and the data can be acquired through a control system of the heat supply first station. Design data for the heating conduit may include thermodynamic performance balance data, valve performance curve data, and safety design data for the conduit.
In some embodiments of the present application, the directly acquired nuclear heating data may be first subjected to data cleansing and complementary processing to ensure the validity of each set of nuclear heating data.
Step 102, determining n target parameters based on a plurality of groups of nuclear energy heat supply data; wherein n is an integer greater than or equal to 2.
In some embodiments of the present application, each set of nuclear heating data includes information of a plurality of parameters, so that a degree of influence of each parameter in each set of nuclear heating data on a nuclear heating load value may be determined based on the plurality of sets of nuclear heating data, and a parameter having a degree of influence greater than a corresponding threshold value is determined as a target parameter.
As a possible implementation, the causal relationship between the parameters may be determined based on the parameters included in each set of nuclear heating data; determining weight values of the heat supply influence of each parameter based on causal relation among the parameters in the plurality of groups of nuclear energy heat supply data; and determining n target parameters according to the weight values of the heat supply influence of each parameter. Wherein, according to the weight value of each parameter to the heat supply influence, the implementation process of determining n target parameters may include: and sequencing the parameters from large to small according to the weight values, and determining the first n parameters in the sequencing result as n target parameters.
For example, the parameters may be causally factored based on sets of nuclear heating data as shown in equation (1):
Xi:＝Fi(PAi，Ui) (1)
wherein Xi represents the father node, namely the nuclear energy heat supply prediction index variable; fi represents a causal determination function; PAi represents the relationship of a specific factor with a random variable Ui in the Xi parent node; ui represents a causal factor; i denotes the i-th node.
After the factorization, the weight value of each parameter on the heat supply influence can be calculated as shown in the formula (2):
wherein t (y→x) represents the degree of influence of the change of the variable y on the variable x, which is the causal strength of the causal relationship from the variable y to the variable x; the variable x is a reason parameter, and the variable x takes a certain parameter in nuclear energy heat supply data. And the variable y is a result parameter, and the variable y takes all parameters in nuclear energy heat supply data.Indicating the sampling period as i, the variable x i+1 Variable->Variable->Probability of occurrence, variable x i The value of the cause parameter is given when the sampling period is i, and the variable x is i+1 The variable y is the value of the cause parameter when the sampling period is i+1 i The variable takes the value of the result parameter when the sampling period is iAs variable x i The kth subsequent variable of (2), variable +.>As variable y i Is the first precursor variable of (c). />Expressed in the known +.>Under the condition of (2) the variable x i+1 Probability of occurrence. />Expressed in the known +.>Under the condition of (2) the variable x i+1 Probability of occurrence.
Step 103, obtaining parameter values of each target parameter.
In some embodiments of the present application, the parameter value of each target parameter obtained may be a parameter value of each target parameter at the current time for predicting the current nuclear heating load. The parameter value of each target parameter obtained may also be a parameter value at a future time, for example, may be a parameter value of the target parameter at a time closest to the time to be predicted. The parameter values at what times the target parameters are, in particular, here, can be determined based on the actual predicted need.
As an example, if the target parameters include a heating area, a weather maximum temperature, a weather minimum temperature, and a resident room temperature, if the nuclear heating load of the open day is predicted, the heating area may be directly used for the heating area at the current time since the heating area in the target parameters is a value with a low frequency of change at the time of prediction, the weather maximum temperature and the weather minimum temperature may be obtained based on weather forecast data, and the resident room temperature may be temperature trend analysis based on the resident room temperature, and the open day room temperature data is determined, so that the prediction of the nuclear heating load of step 104 is performed by the above data.
And 104, obtaining the predicted value of the nuclear energy heat supply load according to the parameter values of the n target parameters and the multiple groups of nuclear energy heat supply data.
In some embodiments of the present application, a training sample may be determined based on a plurality of sets of nuclear power heating data and n target parameters, and the neural network model may be trained based on the determined training sample, so that the neural network model learns to obtain a correspondence between a parameter value of the target parameter and the nuclear power heating load, to obtain a nuclear power heating load prediction model. And during prediction, inputting parameter values of n target parameters into a nuclear energy heating load prediction model to obtain a predicted value of the nuclear energy heating load.
In other embodiments of the present application, a set of target nuclear heating data matching the parameter values of the n target parameters may be determined based on the parameter values of the n target parameters and the plurality of sets of nuclear heating data, and the nuclear heating load value in the target nuclear heating data may be determined as the predicted value of the nuclear heating load.
In practical application, the operation condition of the nuclear power unit can be adjusted based on the predicted value of the nuclear energy heat supply load and the target nuclear energy heat supply data so as to improve the operation safety and economy of the unit.
According to the nuclear energy heat supply load prediction method provided by the embodiment of the application, n target parameters are determined based on a plurality of groups of nuclear energy heat supply data, the parameter value of each target parameter is obtained, and the predicted value of the nuclear energy heat supply load is obtained according to the parameter value of each target parameter and the plurality of groups of nuclear energy heat supply data. According to the scheme, the prediction of the nuclear energy heat supply load based on the history nuclear energy heat supply data can be realized, so that the operation strategy of the nuclear energy heat supply unit can be adjusted based on the nuclear energy load predicted value, the efficient utilization of nuclear energy can be improved, and the unit operation and the safety and reliability of external heat supply can be ensured.
Next, a process for obtaining a predicted value of the nuclear heating load will be described in detail.
Fig. 2 is a flowchart of another method for predicting a nuclear heating load according to an embodiment of the present application. As shown in fig. 2, based on the above embodiment, the implementation procedure of step 104 in fig. 1 may include the following steps:
step 201, determining a set of target nuclear power heating data matched with parameter values of n target parameters from a plurality of sets of nuclear power heating data.
In some embodiments of the present application, for each set of nuclear power heating data, differences between parameter values of n target parameters and parameter values of corresponding parameters in the nuclear power heating data are calculated, respectively, and a set of nuclear power heating data corresponding to a case where the differences are minimum is determined as the target nuclear power heating data.
In other embodiments of the present application, the euclidean distance between the nuclear heating data and the parameter values of the n target parameters may be calculated for each set of the nuclear heating data; and determining a group of nuclear energy heat supply data with the minimum Euclidean distance between the parameter values of the n target parameters as target nuclear energy heat supply data.
As an example, the euclidean distance between the nuclear heating data and the parameter values of the n target parameters may be calculated by the following formula (3):
wherein d j The Euclidean distance between the j-th group of nuclear energy heat supply data and the parameter values of n target parameters; xi is the parameter value of the ith target parameter in n target parameters; yi is the parameter value of the ith target parameter in the jth group of nuclear energy heat supply data.
Step 202, according to the target nuclear energy heat supply data, determining the predicted value of the nuclear energy heat supply load.
It will be appreciated that the target nuclear heating data is the data closest to the nuclear heating conditions to be predicted, so the nuclear heating load value in the target nuclear heating data is also closest to the nuclear heating load to be predicted.
As an example, from the target nuclear heating data, the implementation of determining the predicted value of the nuclear heating load may be: and determining the nuclear energy heating load value in the target nuclear energy heating data as a predicted value of the nuclear energy heating load.
According to the nuclear energy heat supply load prediction method provided by the embodiment of the application, a group of target nuclear energy heat supply data matched with the parameter values of n target parameters is determined from a plurality of groups of nuclear energy common data, and the predicted value of the nuclear energy heat supply load is determined according to the target nuclear energy heat supply data, so that the predicted value of the nuclear energy heat supply load can be accurately determined based on historical data, and a reliable basis is provided for adjusting the operation strategy of the nuclear energy heat supply unit.
In order to achieve the above embodiments, the present application provides a prediction apparatus of a nuclear heating load.
Fig. 3 is a block diagram of a prediction apparatus for nuclear heating load according to an embodiment of the present application. As shown in fig. 3, the apparatus includes:
a first obtaining module 310, configured to obtain a plurality of groups of nuclear energy heating data in a historical period; each group of nuclear energy heat supply data comprises nuclear energy heat supply operation data and heat supply load values under corresponding sampling time;
a determining module 320, configured to determine n target parameters based on a plurality of groups of nuclear heating data; wherein n is an integer greater than or equal to 2;
a second obtaining module 330, configured to obtain a parameter value of each target parameter;
and a third obtaining module 340, configured to obtain a predicted value of the nuclear heating load according to the parameter values of the n target parameters and the multiple sets of nuclear heating data.
Wherein the nuclear heating operation data comprises at least one of: resident side heat supply data, operation condition data of the secondary station, meteorological data, operation state data of the heat supply first station and design data of the heat supply pipeline.
In some embodiments of the present application, the determining module 320 is specifically configured to:
determining weight values of heat supply influences of all parameters based on causal relations among all parameters in the plurality of groups of nuclear energy heat supply data;
and determining n target parameters according to the weight values of the heat supply influence of each parameter.
As one possible implementation, the determining module 320 is further configured to:
sequencing all parameters according to the weight values from large to small;
the first n parameters in the ranking result are determined as n target parameters.
In some embodiments of the present application, the third acquisition module 340 includes:
a first determining unit 341, configured to determine, from the multiple sets of nuclear power heating data, a set of target nuclear power heating data that matches parameter values of n target parameters;
a second determining unit 342 for determining a predicted value of the nuclear heating load based on the target nuclear heating data.
As a possible implementation manner, the first determining unit 341 is specifically configured to:
for each group of nuclear energy heat supply data, calculating Euclidean distances between the nuclear energy heat supply data and parameter values of n target parameters;
and determining a group of nuclear energy heat supply data with the minimum Euclidean distance between the parameter values of the n target parameters as target nuclear energy heat supply data.
As a possible implementation manner, the second determining unit 342 is specifically configured to:
and determining the heat supply load value in the target nuclear heat supply data as a predicted value of the nuclear heat supply load.
According to the nuclear energy heat supply load prediction device provided by the embodiment of the application, a plurality of groups of nuclear energy heat supply data in a historical time period are acquired, n target parameters are determined based on the plurality of groups of nuclear energy heat supply data, the parameter value of each target parameter is acquired, and the predicted value of the nuclear energy heat supply load is acquired according to the parameter value of each target parameter and the plurality of groups of nuclear energy heat supply data. According to the scheme, the prediction of the nuclear energy heat supply load based on the history nuclear energy heat supply data can be realized, so that the operation strategy of the nuclear energy heat supply unit can be adjusted based on the nuclear energy load predicted value, the efficient utilization of nuclear energy can be improved, and the unit operation and the safety and reliability of external heat supply can be ensured.
It should be noted that the foregoing explanation of the embodiment of the method for predicting a nuclear heating load is also applicable to the device for predicting a nuclear heating load of this embodiment, and will not be repeated here.
In order to achieve the above embodiments, the present application provides an electronic device.
Fig. 4 is a block diagram of an electronic device according to an embodiment of the present application. The electronic device may be a server, a computer, or the like. As shown in fig. 4, the electronic device includes:
memory 410 and processor 420, bus 430 connecting the different components (including memory 410 and processor 420), memory 410 storing processor 420 executable instructions; wherein the processor 420 is configured to execute the instructions to implement the method of predicting a nuclear heating load according to an embodiment of the present disclosure.
Bus 430 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Electronic device 400 typically includes a variety of electronic device readable media. Such media can be any available media that is accessible by electronic device 400 and includes both volatile and nonvolatile media, removable and non-removable media. Memory 410 may also include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 440 and/or cache memory 450. Electronic device 400 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 440 may be used to read from or write to a non-removable, non-volatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard disk drive"). Although not shown in fig. 4, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 430 through one or more data medium interfaces. Memory 410 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the various embodiments of the disclosure.
A program/utility 480 having a set (at least one) of program modules 470 may be stored, for example, in memory 410, such program modules 470 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 470 generally perform the functions and/or methods in the embodiments described in this disclosure.
The electronic device 400 may also communicate with one or more external devices 490 (e.g., keyboard, pointing device, display 491, etc.), with one or more devices that enable a user to interact with the electronic device 400, and/or with any device (e.g., network card, modem, etc.) that enables the electronic device 400 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 494. Also, electronic device 400 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 493. As shown, network adapter 493 communicates with other modules of electronic device 400 over bus 430. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 400, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processor 420 executes various functional applications and data processing by running programs stored in the memory 410.
It should be noted that, the implementation process and the technical principle of the electronic device in this embodiment refer to the foregoing explanation of the method for predicting the nuclear heating load in the embodiment of the disclosure, and are not repeated herein.
In order to implement the above-described embodiments, the present disclosure also proposes a computer storage medium.
Wherein the instructions in the storage medium, when executed by the processor of the server, enable the server to perform the method of predicting nuclear heating load as described above. Alternatively, the computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. A method for predicting nuclear heating load, comprising:
acquiring a plurality of groups of nuclear energy heat supply data in a historical time period; each group of nuclear energy heat supply data comprises nuclear energy heat supply operation data and heat supply load values under corresponding sampling time;
determining n target parameters based on the plurality of groups of nuclear energy heating data; wherein n is an integer greater than or equal to 2;
acquiring a parameter value of each target parameter;
and obtaining the predicted value of the nuclear energy heat supply load according to the parameter values of the n target parameters and the multiple groups of nuclear energy heat supply data.
2. The method of claim 1, wherein the nuclear heating operation data comprises at least one of: resident side heat supply data, operation condition data of the secondary station, meteorological data, operation state data of the heat supply first station and design data of the heat supply pipeline.
3. The method of claim 1, wherein the determining n target parameters based on the plurality of sets of nuclear heating data comprises:
determining weight values of the influence of each parameter on heat supply based on causal relations among the parameters in the plurality of groups of nuclear energy heat supply data;
and determining the n target parameters according to the weight values of the parameters on the heat supply.
4. A method according to claim 3, wherein said determining said n target parameters based on the weight values of said respective parameters for heating effects comprises:
sequencing the parameters from big to small according to the weight values;
and determining the first n parameters in the sequencing result as the n target parameters.
5. The method of claim 1, wherein the obtaining a predicted value of the nuclear heating load based on the parameter values of the n target parameters and the plurality of sets of nuclear heating data comprises:
determining a set of target nuclear energy heat supply data matched with the parameter values of the n target parameters from the plurality of sets of nuclear energy heat supply data;
and determining a predicted value of the nuclear energy heating load according to the target nuclear energy heating data.
6. The method of claim 5, wherein said determining a set of target nuclear heating data that matches parameter values of said n target parameters from said plurality of sets of nuclear heating data comprises:
for each group of nuclear energy heat supply data, calculating Euclidean distances between the nuclear energy heat supply data and parameter values of the n target parameters;
and determining a group of nuclear energy heat supply data with the minimum Euclidean distance between the parameter values of the n target parameters as the target nuclear energy heat supply data.
7. The method of claim 5, wherein said determining a predicted value of said nuclear heating load based on said target nuclear heating data comprises:
and determining the heat supply load value in the target nuclear energy heat supply data as the predicted value of the nuclear energy heat supply load.
8. A nuclear heating load prediction apparatus, comprising:
the first acquisition module is used for acquiring a plurality of groups of nuclear energy heat supply data in a historical time period; each group of nuclear energy heat supply data comprises nuclear energy heat supply operation data and heat supply load values under corresponding sampling time;
the determining module is used for determining n target parameters based on the plurality of groups of nuclear energy heat supply data; wherein n is an integer greater than or equal to 2;
the second acquisition module is used for acquiring the parameter value of each target parameter;
and the third acquisition module is used for acquiring the predicted value of the nuclear energy heat supply load according to the parameter values of the n target parameters and the plurality of groups of nuclear energy heat supply data.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 7 when the program is executed by the processor.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1 to 7.
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