CN115630584B - Artificial intelligence-based energy analysis method and system - Google Patents
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Abstract
The invention discloses an artificial intelligence-based energy analysis method and system. 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 the energy data into a pre-constructed energy analysis model, and outputting energy loss; and comparing the output energy loss with an energy loss standard value preset by an energy system, if the energy loss is larger than the energy loss standard value, determining that the energy loss of the energy workstation exceeds the standard, dispatching an expert to repair the energy loss, and if the energy loss is smaller 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 as to guide technicians to carry out energy consumption restoration of the corresponding workstation.
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, and generally refers to thermal energy, electric energy, optical energy, mechanical energy, chemical energy and the like, and can provide kinetic energy, mechanical energy and energy for human beings.
Along with the daily and monthly variation of science and technology, the global utilization of energy is also more and more widespread, each energy workstation uses more than one energy, if the loss of the energy is higher in the use process, the energy may have the problems of insufficient combustion and the like caused by equipment problems, the existing energy analysis method is generally comprehensively evaluated by experienced technicians, and the rough estimation of whether the energy of the workstation meets the standard is performed, so how to quickly find the workstation exceeding the loss standard is a technical problem of the energy technicians.
Disclosure of Invention
The invention provides an artificial intelligence-based energy analysis method, which comprises the following steps:
receiving energy data uploaded by each energy workstation;
inputting the energy data into a pre-constructed energy analysis model, and outputting energy loss;
and comparing the output energy loss with an energy loss standard value preset by an energy system, if the energy loss is larger than the energy loss standard value, determining that the energy loss of the energy workstation exceeds the standard, dispatching an expert to repair the energy loss, and if the energy loss is smaller than the energy loss standard value, determining that the energy loss of the energy workstation is normal.
According to the artificial intelligence-based energy analysis method, a plurality of energy sources are used in each energy workstation, wherein the energy sources comprise 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 energy workstation attributes, IP addresses, various energy input amounts and output amounts, wherein the input amounts are the input weight in each energy T time, and the output amounts are the output weight in each energy T time.
An artificial intelligence based energy analysis method as described above, wherein the energy workstation attributes include workstation identification, hardware security level, software security level, network security level.
According to the artificial intelligence-based energy analysis method, 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 construction of the energy analysis model specifically includes:
acquiring stored historical energy data and corresponding energy loss, 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 artificial intelligence-based energy analysis system, which comprises an energy analysis platform and a plurality of energy workstations;
the energy workstations are used for collecting energy data of the respective energy workstations;
the energy analysis platform 22 is configured to receive energy data uploaded by each energy workstation; inputting the energy data into a pre-constructed energy analysis model, and outputting energy loss; and comparing the output energy loss with an energy loss standard value preset by an energy system, if the energy loss is larger than the energy loss standard value, determining that the energy loss of the energy workstation exceeds the standard, dispatching an expert to repair the energy loss, and if the energy loss is smaller 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 several energy sources are used in each energy workstation, including solar energy, optical energy, nuclear energy, ocean energy, tidal energy, wind energy, biomass energy, hydrogen energy, uranium energy, water energy; the energy data comprises energy workstation attributes, IP addresses, various energy input amounts and output amounts, wherein the input amounts are the input weight in each energy T time, and the output amounts are the output weight in each energy T time.
An artificial intelligence based energy analysis system as described above wherein the energy workstation attributes include workstation identification, hardware security level, software security level, network security level.
The energy analysis system based on artificial intelligence, as described above, wherein when the energy workstation uploads the energy data to the energy analysis platform, the energy analysis platform verifies the security of the energy workstation according to the energy data.
The energy analysis system based on artificial intelligence, as described above, wherein an energy analysis model is constructed, specifically for:
acquiring stored historical energy data and corresponding energy loss, 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 beneficial effects achieved by the invention are as follows: 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 as to guide technicians to carry out energy consumption restoration of the corresponding workstation.
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 description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of an artificial intelligence based energy analysis method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an artificial intelligence-based energy analysis system according to a second embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, a first embodiment of the present invention provides an artificial intelligence-based energy analysis method applied to an energy analysis platform, the method comprising:
the energy analysis platform provided by the application can receive the energy data uploaded by the energy workstations in different areas, and each energy workstation can use a plurality of energy sources such as solar energy, light energy, nuclear energy, ocean energy, tidal energy, wind energy, biomass energy, hydrogen energy, uranium energy, water energy and the like. The energy data comprises energy workstation attributes (including workstation identification, hardware security level, software security level, network security level and the like), IP addresses, various energy input amounts and output amounts, wherein the input amounts are the input weight of each energy T in time, and the output amounts are the output weight of each energy T in time.
specifically, in order to prevent the energy data from being tampered during the transmission process, when the energy workstation uploads the energy data to the energy analysis platform, the energy analysis platform firstThe method for verifying the safety of the energy workstation specifically comprises the following steps: the energy analysis platform adopts a formula
Calculating a safety value of the energy station, wherein +.>
For the safety of energy workstations +.>
Indicating whether the address of the energy workstation is an illegal address field of the energy analysis platform, if not, the address is +.>
1, if yes, then->
Is 0; />
The method comprises the steps that the correlation factor between an energy workstation and an energy analysis platform is a value given by a system when the energy workstation registers on the energy analysis platform, and the value is obtained through identification inquiry of the energy workstation; />
The i security factor of the energy workstation comprises a hardware security level, a software security level and a network security level, wherein the i is 1 to N, and N is the total number of the security factors; />
The influence weight of the ith safety factor on the safety of the energy work station is given.
After verifying the energy workstation's safety, constructing various energy production values and energies in the energy data into an energy data set
The method for constructing the energy analysis model specifically comprises the following steps of:
step S1, acquiring stored historical energy data and corresponding energy loss, and constructing an energy vector set and a loss vector set;
specifically, the obtained historical energy data and the corresponding energy loss amount construct an energy vector set as
The loss vector set is->
Wherein->
In order to obtain the input and output of the j-th energy in the i-th 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 energy type number; />
Corresponding to the acquired ith group of dataEnergy consumption of the fuel cell system.
S2, inputting the energy vector set and the loss vector set into a machine model for training;
s3, outputting a 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 (1) outputting energy loss, wherein ∈>
For the influence weight of the kth energy source on the energy loss, < ->
Is the input amount of the kth energy source, < >>
For the production of k-th energy, < > th energy source>
In order to measure the value of the deviation,
and 130, comparing the output energy loss with an energy loss standard value preset by an energy system, if the energy loss is larger than the energy loss standard value, determining that the energy loss of the energy workstation exceeds the standard, dispatching an expert to repair the energy loss, and if the energy loss is smaller 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, which includes: a plurality of energy workstations 21 (including energy workstations 211, 212 … … 21 n) and an energy analysis platform 22.
The energy workstations 21 are used to collect energy data for the respective energy workstations. Several energy sources are used in each energy station, such as 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 energy workstation attributes (including workstation identification, hardware security level, software security level, network security level and the like), IP addresses, various energy input amounts and output amounts, wherein the input amounts are the input weight of each energy T in time, and the output amounts are the output weight of each energy T in time.
The energy analysis platform 22 is configured to receive energy data uploaded by each energy workstation; inputting the energy data into a pre-constructed energy analysis model, and outputting energy loss; and comparing the output energy loss with an energy loss standard value preset by an energy system, if the energy loss is larger than the energy loss standard value, determining that the energy loss of the energy workstation exceeds the standard, dispatching an expert to repair the energy loss, and if the energy loss is smaller than the energy loss standard value, determining that the energy loss of the energy workstation is normal.
Specifically, in order to prevent the energy data from being tampered in the transmission process, after 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 specifically includes: the energy analysis platform adopts a formula
Calculating a safety value of the energy station, wherein +.>
For the safety of energy workstations +.>
Indicating whether the address of the energy workstation is an illegal address field of the energy analysis platform, if not, the address is +.>
1, if yes/>
Is 0; />
The method comprises the steps that the correlation factor between an energy workstation and an energy analysis platform is a value given by a system when the energy workstation registers on the energy analysis platform, and the value is obtained through identification inquiry of the energy workstation; />
The i security factor of the energy workstation comprises a hardware security level, a software security level and a network security level, wherein the i is 1 to N, and N is the total number of the security factors; />
The influence weight of the ith safety factor on the safety of the energy work station is given.
After verifying the energy workstation's safety, constructing various energy production values and energies in the energy data into an energy data set
Wherein->
Input and output of first energy for energy workstation, +.>
Input and output of second energy for energy workstation, +.>
Input and output of ith energy for energy workstation, +.>
Input amount of nth energy for energy workstationAnd output, n is the total number of energy species.
The method comprises the steps of constructing an energy analysis model, and particularly, acquiring stored historical energy data and corresponding energy loss, 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 a trained energy analysis model;
specifically, the obtained historical energy data and the corresponding energy loss amount construct an energy vector set as
The loss vector set is->
Wherein->
In order to obtain the input and output of the j-th energy in the i-th 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 energy type number; />
And (5) obtaining the energy consumption corresponding to the ith group of data.
Inputting the energy vector set and the loss vector set into a machine model, and training to obtain an energy analysis model
In (1) outputting energy loss, wherein ∈>
For the influence weight of the kth energy source on the energy loss, < ->
Is the input amount of the kth energy source, < >>
For the production of k-th energy, < > th energy source>
In order to measure the value of the deviation,
corresponding to the above embodiment, the embodiment of the present invention provides an artificial intelligence based energy analysis platform, 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 for performing an artificial intelligence based energy analysis method.
In accordance with the foregoing embodiments, the 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 an artificial intelligence based energy analysis method.
The disclosed embodiments provide a computer readable storage medium having stored therein computer program instructions that, when executed on a computer, cause the computer to perform an artificial intelligence based energy analysis method as described above.
In the embodiment of the invention, the processor may be an integrated circuit chip with signal processing capability. The processor may be a general purpose processor, a digital signal processor (Digital Signal Processor, DSP for short), an application specific integrated circuit (Application Specific f ntegrated Circuit ASIC for short), a field programmable gate array (FieldProgrammable Gate Array FPGA for short), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.
The disclosed methods, steps, and logic blocks 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 embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The processor reads the information in the storage medium and, in combination with its hardware, performs the steps of the above method.
The storage medium may be memory, for example, 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 ROM (Electrically EPROM, EEPROM), or a flash Memory.
The volatile memory may be a random access memory (Random Access Memory, RAM for short) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (Double Data RateSDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (SLDRAM), and direct memory bus RAM (directracram, DRRAM).
The storage media described in embodiments of the present 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 in one or more of the examples described above, the functions described in the present invention may be implemented in a combination of hardware and software. When the software is applied, the corresponding functions may be stored in a computer-readable medium or transmitted as one or more instructions or code on the 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 foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention in further detail, and are not to be construed as limiting the scope of the invention, but are merely intended to cover any modifications, equivalents, improvements, etc. based on the teachings of the invention.
Claims (8)
1. An artificial intelligence based energy analysis method, comprising:
receiving energy data uploaded by each energy workstation;
inputting the energy data into a pre-constructed energy analysis model, and outputting energy loss;
comparing the output energy loss with an energy loss standard value preset by an energy system, if the energy loss is larger than the energy loss standard value, determining that the energy loss of the energy workstation exceeds the standard value, dispatching an expert to repair the energy loss, and if the energy loss is smaller than the energy loss standard value, determining that the energy loss of the energy workstation is normal;
the construction of the energy analysis model specifically comprises the following steps:
step S1, acquiring stored historical energy data and corresponding energy loss, and constructing an energy vector set and a loss vector set;
specifically, the obtained historical energy data and the corresponding energy loss amount construct an energy vector set as
The loss vector set is->
Wherein->
In order to obtain the input and output of the j-th energy in the i-th 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 energy type number; />
The energy consumption corresponding to the obtained ith group of data;
s2, inputting the energy vector set and the loss vector set into a machine model for training;
s3, outputting a 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
Output energy loss, wherein +.>
For the influence weight of the kth energy source on the energy loss, < ->
Is the input amount of the kth energy source, < >>
For the production of k-th energy, < > th energy source>
For measuring the deviation value +.>
。
2. The artificial intelligence based energy analysis method of claim 1, wherein a plurality of energy sources are used in each energy workstation, 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 energy workstation attributes, IP addresses, various energy input amounts and output amounts, wherein the input amounts are the input weight in each energy T time, and the output amounts are the output weight in each energy T time.
3. The artificial intelligence based energy analysis method of claim 2, wherein the energy workstation attributes include workstation identification, 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 based on the energy data when the energy workstation uploads the energy data to the energy analysis platform.
5. An artificial intelligence-based energy analysis system is characterized by comprising an energy analysis platform and a plurality of energy workstations;
the energy workstations are used for collecting energy data of the respective energy workstations;
the energy analysis platform is used for receiving the energy data uploaded by each energy workstation; inputting the energy data into a pre-constructed energy analysis model, and outputting energy loss; comparing the output energy loss with an energy loss standard value preset by an energy system, if the energy loss is larger than the energy loss standard value, determining that the energy loss of the energy workstation exceeds the standard value, dispatching an expert to repair the energy loss, and if the energy loss is smaller than the energy loss standard value, determining that the energy loss of the energy workstation is normal;
the construction of the energy analysis model specifically comprises the following steps:
step S1, acquiring stored historical energy data and corresponding energy loss, and constructing an energy vector set and a loss vector set;
specifically, the obtained historical energy data and the corresponding energy loss amount construct an energy vector set as
The loss vector set is->
Wherein->
In order to obtain the input and output of the j-th energy in the i-th 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 energy type number; />
The energy consumption corresponding to the obtained ith group of data;
s2, inputting the energy vector set and the loss vector set into a machine model for training;
s3, outputting a 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 (1) outputting energy loss, wherein ∈>
For the influence weight of the kth energy source on the energy loss, < ->
Is the input amount of the kth energy source, < >>
For the production of k-th energy, < > th energy source>
For measuring the deviation value +.>
。
6. The artificial intelligence based energy analysis system of claim 5, wherein a plurality of energy sources are used in each energy workstation, 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 energy workstation attributes, IP addresses, various energy input amounts and output amounts, wherein the input amounts are the input weight in each energy T time, and the output amounts are the output weight in each energy T time.
7. The artificial intelligence based energy analysis system of claim 6, wherein the energy workstation attributes include workstation identification, hardware security level, software security level, network security level.
8. The artificial intelligence based energy analysis system of claim 5, wherein the energy analysis platform verifies the security of the energy workstation based on the energy data as the energy workstation uploads the energy data to the energy analysis platform.
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