CN115630584A - Energy analysis method and system based on artificial intelligence - Google Patents
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Abstract
The invention discloses an energy analysis method and system based on artificial intelligence. The system comprises an energy analysis platform and a plurality of energy workstations; the energy analysis method comprises the steps of receiving energy data uploaded by each energy workstation; inputting energy data into a pre-constructed energy analysis model, and outputting energy consumption; comparing the output energy loss with an energy loss standard value preset by an energy system, if the energy loss is greater than the energy loss standard value, determining that the energy loss of the energy workstation exceeds the standard, dispatching a specialist for energy loss repair, and if the energy loss is less than the energy loss standard value, determining that the energy loss of the energy workstation is normal. By adopting the technical scheme provided by the invention, the energy consumption of each workstation can be accurately calculated according to the data uploaded by the energy workstations, so that technicians are guided to carry out energy consumption restoration on the corresponding workstations.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an energy analysis method and system based on artificial intelligence.
Background
The energy source refers to a resource capable of providing energy, generally refers to heat energy, electric energy, light energy, mechanical energy, chemical energy and the like, and can provide kinetic energy, mechanical energy and energy for human beings.
With the change of science and technology, energy sources are widely used in the world, each energy source workstation uses more than one energy source, if the energy sources are high in loss in the using process, the energy sources may have the problems of insufficient combustion and the like caused by equipment problems, the existing energy source analysis method is generally comprehensively evaluated by experienced technicians, whether the energy sources of the workstations meet the standards is roughly estimated, and therefore how to quickly find the workstations exceeding the loss standards is a technical problem which is paid attention by energy technicians.
Disclosure of Invention
The invention provides an energy analysis method based on artificial intelligence, which comprises the following steps:
receiving energy data uploaded by each energy workstation;
inputting energy data into a pre-constructed energy analysis model, and outputting energy consumption;
comparing the output energy loss with an energy loss standard value preset by an energy system, if the energy loss is greater than the energy loss standard value, determining that the energy loss of the energy workstation exceeds the standard, dispatching a specialist for energy loss repair, and if the energy loss is less than the energy loss standard value, determining that the energy loss of the energy workstation is normal.
An artificial intelligence based energy analysis method as described above, wherein each energy workstation uses several energy sources, including solar energy, light energy, nuclear energy, ocean energy, tidal energy, wind energy, biomass energy, hydrogen energy, uranium energy, and water energy; the energy data comprises the attribute of the energy workstation, the IP address, the input amount and the output amount of various energy sources, wherein the input amount is the weight input in each energy source T time, and the output amount is the weight output in each energy source T time.
The energy analysis method based on artificial intelligence is characterized in that the energy workstation attributes comprise workstation identification, hardware security level, software security level and network security level.
The energy analysis method based on artificial intelligence is characterized in that when the energy workstation uploads the energy data to the energy analysis platform, the energy analysis platform verifies the safety of the energy workstation according to the energy data.
The energy analysis method based on artificial intelligence as described above, wherein the constructing of the energy analysis model specifically includes:
acquiring stored historical energy data and corresponding energy consumption, and constructing an energy vector set and a loss vector set;
inputting the energy vector set and the loss vector set into a machine model for training;
and outputting the trained energy analysis model.
The invention also provides an energy analysis system based on artificial intelligence, which comprises an energy analysis platform and a plurality of energy workstations;
the energy work stations are used for collecting energy data of the energy work stations;
the energy analysis platform 22 is used for receiving energy data uploaded by each energy workstation; inputting energy data into a pre-constructed energy analysis model, and outputting energy consumption; comparing the output energy loss with an energy loss standard value preset by an energy system, if the energy loss is greater than the energy loss standard value, determining that the energy loss of the energy workstation exceeds the standard, dispatching a specialist for energy loss repair, and if the energy loss is less than the energy loss standard value, determining that the energy loss of the energy workstation is normal.
An artificial intelligence based energy analysis system as described above, wherein each energy workstation uses several energy sources, including solar energy, light energy, nuclear energy, ocean energy, tidal energy, wind energy, biomass energy, hydrogen energy, uranium energy, and water energy; the energy data comprises the attribute of the energy workstation, the IP address, the input amount and the output amount of various energy sources, wherein the input amount is the weight input in each energy source T time, and the output amount is the weight output in each energy source T time.
An energy analysis system based on artificial intelligence as above, wherein the energy workstation attributes include workstation identification, hardware security level, software security level, and network security level.
The energy analysis system based on artificial intelligence is characterized in that when the energy workstation uploads the energy data to the energy analysis platform, the energy analysis platform verifies the safety of the energy workstation according to the energy data.
The artificial intelligence-based energy analysis system as described above, wherein the energy analysis model is constructed, and is specifically configured to:
acquiring stored historical energy data and corresponding energy consumption, and constructing an energy vector set and a loss vector set;
inputting the energy vector set and the loss vector set into a machine model for training;
and outputting the trained energy analysis model.
The invention has the following beneficial effects: by adopting the technical scheme provided by the invention, the energy consumption of each workstation can be accurately calculated according to the data uploaded by the energy workstations, so that technicians are guided to carry out energy consumption restoration on the corresponding workstations.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained by those skilled in the art according to these drawings.
Fig. 1 is a flowchart of an energy analysis method based on artificial intelligence according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an energy analysis system based on artificial intelligence according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Example one
As shown in fig. 1, an embodiment of the present invention provides an artificial intelligence-based energy analysis method applied in an energy analysis platform, where the method includes:
110, receiving energy data uploaded by each energy workstation;
the energy analysis platform that this application provided can receive the energy data that the energy workstation in different regions uploaded, can use a plurality of kinds of energy in every energy workstation, like solar energy, light energy, nuclear energy, ocean energy, tidal energy, wind energy, biomass energy, hydrogen energy, uranium energy, water energy etc.. The energy data comprises the attributes of the energy workstation (including workstation identification, hardware security level, software security level, network security level and the like), IP addresses, various energy input quantities and output quantities, wherein the input quantities are the weight input in each energy T time, and the output quantities are the weight output in each energy T time.
specifically, in order to prevent the energy data from being tampered in the transmission process, when the energy workstation uploads the energy data to the energy analysis platform, the energy analysis platform firstly verifies the security of the energy workstation, and the method specifically comprises the following steps: formula adopted by energy analysis platform
Calculating a safety value for the energy workstation, wherein,
in order to provide security for the energy workstation,
whether the address of the energy workstation is an illegal address domain of the energy analysis platform is shown, and if not, the address is judged to be illegal
Is 1, if so, then
Is 0;
the correlation factor is the correlation factor of the energy workstation and the energy analysis platform, the value is the value given by the system when the energy workstation registers on the energy analysis platform, and the value is obtained by inquiring the identification of the energy workstation;
the ith safety factor is the energy workstation, and comprises a hardware safety level, a software safety level and a network safety level, wherein the value of i is 1 to N, and N is the total number of the safety factors;
the impact of the ith safety factor on the safety of the energy workstation is weighted.
After the safety of the energy workstation is verified, various energy source production values in the energy data and the energy are constructed into an energy source data set
The energy analysis model is constructed, and the method specifically comprises the following steps:
s1, acquiring stored historical energy data and corresponding energy consumption, and constructing an energy vector set and a loss vector set;
specifically, the obtained historical energy data and the corresponding energy consumption amount construct an energy vector set as
Set of loss vectors as
Wherein, in the step (A),
in order to obtain the input amount and the output amount of the jth energy in the ith group of energy data, the value of i is 1 to m, m is the total number of the energy data, the value of j is 1 to n, and n is the number of energy types;
and obtaining the energy consumption corresponding to the ith group of data.
S2, inputting the energy vector set and the loss vector set into a machine model for training;
s3, outputting the trained energy analysis model;
inputting the energy vector set and the loss vector set into a machine model, and training to obtain an energy analysis model
In the output of the energy consumption, wherein,
to weight the impact of the kth energy source on energy loss,
the input amount of the kth energy source,
for the output of the kth energy source,
in order to measure the deviation value,
and step 130, comparing the output energy loss with an energy loss standard value preset by an energy system, if the energy loss is greater than the energy loss standard value, determining that the energy loss of the energy workstation exceeds the standard, dispatching a specialist for energy loss repair, and if the energy loss is less than the energy loss standard value, determining that the energy loss of the energy workstation is normal.
Example two
As shown in fig. 2, a second embodiment of the present invention provides an artificial intelligence-based energy analysis system 2, including: a plurality of energy workstations 21 (including energy workstations 211, 212, 8230; 21 n) and an energy analysis platform 22.
The energy workstations 21 are used to collect energy data from the respective energy workstations. Several energy sources are used in each energy station, such as solar, light, nuclear, ocean, tidal, wind, biomass, hydrogen, uranium, water, etc. The energy data comprises the attributes of the energy work station (including work station identification, hardware safety level, software safety level, network safety level and the like), IP addresses, input quantity of various energy sources and output quantity, wherein the input quantity is the weight input in each energy source T time, and the output quantity is the weight output in each energy source T time.
The energy analysis platform 22 is used for receiving energy data uploaded by each energy workstation; inputting energy data into a pre-constructed energy analysis model, and outputting energy consumption; comparing the output energy loss with an energy loss standard value preset by an energy system, if the energy loss is greater than the energy loss standard value, determining that the energy loss of the energy workstation exceeds the standard, dispatching a specialist for energy loss repair, and if the energy loss is less than the energy loss standard value, determining that the energy loss of the energy workstation is normal.
Specifically, to prevent the energy data from being tampered during transmission, the energy data is analyzed from the energy workstation to the energy sourceAfter the platform uploads the energy data, the energy analysis platform firstly verifies the safety of the energy workstation, and the method specifically comprises the following steps: formula adopted by energy analysis platform
Calculating a safety value for the energy workstation, wherein,
in order to provide security for the energy workstation,
whether the address of the energy workstation is an illegal address domain of the energy analysis platform is shown, and if not, the address of the energy workstation is shown as an illegal address domain of the energy analysis platform
Is 1, if so, then
Is 0;
the correlation factor is the correlation factor of the energy workstation and the energy analysis platform, the value is the value given by the system when the energy workstation registers on the energy analysis platform, and the value is obtained by inquiring the identification of the energy workstation;
the ith safety factor is the energy workstation, and comprises a hardware safety level, a software safety level and a network safety level, wherein the value of i is 1 to N, and N is the total number of the safety factors;
the impact of the ith safety factor on the safety of the energy workstation is weighted.
After the safety of the energy workstation is verified, various energy source production values in the energy data and the energy are constructed into an energy source data set
Wherein, in the step (A),
the input amount and the output amount of the first energy source of the energy work station,
the input and output of the second energy source of the energy work station,
the input quantity and the output quantity of the ith energy source of the energy work station,
the input quantity and the output quantity of the nth energy of the energy workstation are shown, and n is the total number of the energy types.
The energy analysis method comprises the steps of constructing an energy analysis model, specifically obtaining stored historical energy data and corresponding energy consumption, and constructing an energy vector set and a loss vector set; inputting the energy vector set and the loss vector set into a machine model for training; outputting the trained energy analysis model;
specifically, the obtained historical energy data and the corresponding energy consumption amount construct an energy vector set as
Set of loss vectors as
Wherein, in the step (A),
in order to obtain the input amount and the output amount of the jth energy in the ith group of energy data, the value of i is 1 to m, m is the total number of the energy data, the value of j is 1 to n, and n is the number of energy types;
for the acquired i-th group of data correspondenceThe amount of energy loss.
Inputting the energy vector set and the loss vector set into a machine model, and training to obtain an energy analysis model
And the energy loss amount is outputted, wherein,
to weight the impact of the kth energy source on energy loss,
the input amount of the kth energy source,
for the throughput of the kth energy source,
in order to measure the deviation value,
corresponding to the above embodiments, an embodiment of the present invention provides an energy analysis platform based on artificial intelligence, including: at least one memory and at least one processor;
the memory is used for storing one or more program instructions;
a processor for executing one or more program instructions to perform an artificial intelligence based energy analysis method.
In accordance with the embodiments described above, embodiments of the present invention provide a computer-readable storage medium having one or more program instructions embodied therein for execution by a processor of a method for artificial intelligence based energy analysis.
The disclosed embodiments of the present invention provide a computer-readable storage medium having computer program instructions stored therein, which, when run on a computer, cause the computer to perform an artificial intelligence based energy analysis method as described above.
In an embodiment of the invention, the processor may be an integrated circuit chip having signal processing capability. The Processor may be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
The various methods, steps, and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, etc. as is well known in the art. The processor reads the information in the storage medium and completes the steps of the method in combination with the hardware.
The storage medium may be a memory, for example, which may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory.
The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory.
The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), SLDRAM (SLDRAM), and Direct Rambus RAM (DRRAM).
The storage media described in connection with the embodiments of the invention are intended to comprise, without being limited to, these and any other suitable types of memory.
Those skilled in the art will appreciate that the functionality described in the present invention may be implemented in a combination of hardware and software in one or more of the examples described above. When software is applied, the corresponding functionality may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present invention should be included in the scope of the present invention.
Claims (10)
1. An energy analysis method based on artificial intelligence is characterized by comprising the following steps:
receiving energy data uploaded by each energy workstation;
inputting energy data into a pre-constructed energy analysis model, and outputting energy consumption;
comparing the output energy loss with an energy loss standard value preset by an energy system, if the energy loss is greater than the energy loss standard value, determining that the energy loss of the energy workstation exceeds the standard, dispatching a specialist for energy loss repair, and if the energy loss is less than the energy loss standard value, determining that the energy loss of the energy workstation is normal.
2. An artificial intelligence based energy analysis method according to claim 1, wherein each energy workstation uses several energy sources, including solar energy, light energy, nuclear energy, ocean energy, tidal energy, wind energy, biomass energy, hydrogen energy, uranium energy, water energy; the energy data comprises the attribute of the energy workstation, the IP address, the input amount and the output amount of various energy sources, wherein the input amount is the weight input in each energy source T time, and the output amount is the weight output in each energy source T time.
3. An artificial intelligence based energy analysis method as claimed in claim 2, wherein the energy workstation attributes include workstation identity, hardware security level, software security level, network security level.
4. The artificial intelligence based energy analysis method of claim 1, wherein the energy analysis platform verifies the security of the energy workstation according to the energy data when the energy workstation uploads the energy data to the energy analysis platform.
5. The artificial intelligence based energy analysis method of claim 1, wherein constructing the energy analysis model specifically comprises:
acquiring stored historical energy data and corresponding energy consumption, and constructing an energy vector set and a loss vector set;
inputting the energy vector set and the loss vector set into a machine model for training;
and outputting the trained energy analysis model.
6. An energy analysis system based on artificial intelligence is characterized by comprising an energy analysis platform and a plurality of energy workstations;
the energy work stations are used for collecting energy data of the energy work stations;
the energy analysis platform is used for receiving energy data uploaded by each energy workstation; inputting energy data into a pre-constructed energy analysis model, and outputting energy consumption; comparing the output energy loss with an energy loss standard value preset by an energy system, if the energy loss is greater than the energy loss standard value, determining that the energy loss of the energy workstation exceeds the standard, dispatching a specialist for energy loss repair, and if the energy loss is less than the energy loss standard value, determining that the energy loss of the energy workstation is normal.
7. An artificial intelligence based energy analysis system according to claim 6, wherein each energy workstation uses several energy sources, including solar, light, nuclear, ocean, tidal, wind, biomass, hydrogen, uranium, water; the energy data comprises the attribute of the energy workstation, an IP address, the input amount and the output amount of various energy sources, wherein the input amount is the weight input in each energy source T time, and the output amount is the weight output in each energy source T time.
8. An artificial intelligence based energy analysis system according to claim 7, wherein the energy workstation attributes include workstation identity, hardware security level, software security level, network security level.
9. The artificial intelligence based energy analysis system of claim 6, wherein the energy analysis platform verifies the security of the energy workstation based on the energy data while the energy workstation uploads the energy data to the energy analysis platform.
10. The artificial intelligence based energy analysis system of claim 6, wherein the energy analysis model is constructed, in particular for:
acquiring stored historical energy data and corresponding energy consumption, and constructing an energy vector set and a loss vector set;
inputting the energy vector set and the loss vector set into a machine model for training;
and outputting the trained energy analysis model.
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| CN115325287A (en) * | 2022-09-13 | 2022-11-11 | 西南石油大学 | Composite energy pipeline transmission characteristic analysis method |
| CN115498668A (en) * | 2022-09-15 | 2022-12-20 | 国网上海市电力公司 | Optimization method of comprehensive energy system |
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