CN110689451B - Large medical equipment energy consumption prediction method based on artificial intelligence and terminal equipment - Google Patents
Large medical equipment energy consumption prediction method based on artificial intelligence and terminal equipment Download PDFInfo
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Abstract
The invention discloses an artificial intelligence-based large-scale medical equipment energy consumption prediction method and terminal equipment, wherein medical staff count the time of using large-scale medical equipment in a hospital, further input the counted data into a host, then generate a chart by using the host, and divide the time periods of a peak period, a middle peak period and a low peak period of using the large-scale medical equipment, and relates to the technical field of energy conservation of large-scale medical equipment. According to the large-scale medical equipment energy consumption prediction method and the terminal equipment based on artificial intelligence, the service time and the service quantity of large-scale medical equipment are counted through a hospital, then the data of the service time and the service quantity of the large-scale medical equipment are combined, the service quantity of the large-scale medical equipment is divided into time periods by utilizing the terminal equipment, the quantity of the large-scale medical equipment in the middle peak period and the low peak period is reduced, the large-scale medical equipment is prevented from working all the time to consume a large amount of electric quantity, the energy waste is greatly reduced, and the energy consumption of the hospital is saved.
Description
Technical Field
The invention relates to the technical field of energy conservation of large-scale medical equipment, in particular to an artificial intelligence-based energy consumption prediction method for the large-scale medical equipment and terminal equipment.
Background
Medical devices refer to instruments, devices, instruments, materials or other items used alone or in combination in the human body, including the required software. The medical equipment is the most basic element of medical treatment, scientific research, teaching, institutions and clinical discipline work, namely professional medical equipment and household medical equipment. The basic condition of medical equipment for continuously improving the technical level of medical science is also an important mark of the degree of modernization, and the medical equipment becomes an important field of modern medical treatment. The development of medical treatment is largely dependent on the development of instruments, and even in the development of the medical industry, the breakthrough of bottlenecks plays a decisive role. Medical equipment refers to instruments, devices, appliances, materials or other items used alone or in combination in the human body, and also includes required software.
In the aspect of energy consumption of existing large-scale medical equipment, as managers of the large-scale equipment start the equipment on duty, the equipment is in a standby state even if no patient exists, however, the standby power consumption of the large-scale medical equipment is staggering, especially when a large-scale comprehensive hospital has a plurality of medical equipment, the standby power consumption is doubled, the energy is greatly wasted, and the energy consumption of the hospital is increased.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides an artificial intelligence-based energy consumption prediction method for large-scale medical equipment and terminal equipment, and solves the problems that the equipment is always started in the aspect of energy consumption of the existing large-scale medical equipment, the consumed electric quantity is remarkable, the energy is greatly wasted, and the energy consumption of hospitals is increased.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: the large-scale medical equipment energy consumption prediction method based on artificial intelligence specifically comprises the following steps:
step one, equipment time statistics: medical staff counts the time of using large-scale medical equipment in a hospital, then inputs the counted data into a host, then generates a chart by using the host, and divides the time periods of the peak time, the middle peak time and the low peak time of using the large-scale medical equipment;
step two, equipment quantity statistics: medical staff count the number of large medical equipment used by the hospital, and then correspond the data to the peak time, the peak time and the peak time used by the large medical equipment in the step one, and the data are combined and then input into a host, and a chart is generated by using the host;
step three, controlling time and quantity: connecting the large medical equipment with the terminal equipment, further controlling the starting time and the number of the started large medical equipment through the terminal equipment, and then counting the energy consumption data of the large medical equipment and inputting the energy consumption data into the host for storage;
step four, equipment is added in an emergency mode: the large medical equipment which is not started is connected with the artificial intelligence in the terminal equipment, and the quantity of the large medical equipment which needs to be increased is quickly started by the artificial intelligence when the large medical equipment needs to be increased urgently.
Preferably, in the first step, the time of using the large medical equipment by the hospital is counted, the time counting period is one week, and then the average value is obtained.
Preferably, in the second step, the number of the large medical devices used by the hospital is counted, the period of the number usage statistics is one week, and then the average value is obtained.
Preferably, the data obtained by counting the energy consumption data of the large medical equipment in the third step can be read by the chip at any time and then called for analysis.
The invention also discloses terminal equipment which comprises an input module, a central processing system, a time setting module, a driving module, an output module, a feedback module and a voice control unit, wherein the output end of the input module is connected with the input end of the central processing system, and the output end of the central processing system is connected with the input end of the time setting module.
Preferably, the output end of the time setting module is connected with the input end of the driving module, and the output end of the driving module is connected with the input ends of the output module and the feedback module.
Preferably, the output end of the feedback module is connected with the input end of the central processing system, and the output end of the voice control unit is connected with the input end of the driving module.
Preferably, the voice control unit comprises an extraction module, a control chip, a recognition module, a data conversion module and a presentation module.
Preferably, the output end of the extraction module is connected with the input end of the control chip, and the output end of the control chip is connected with the input end of the identification module.
Preferably, the output end of the identification module is connected with the input end of the data conversion module, and the output end of the data conversion module is connected with the input end of the extraction module.
(III) advantageous effects
The invention provides an artificial intelligence-based large-scale medical equipment energy consumption prediction method and terminal equipment. Compared with the prior art, the method has the following beneficial effects:
(1) the large-scale medical equipment energy consumption prediction method based on artificial intelligence and the terminal equipment are characterized in that the method comprises the following steps of: medical staff counts the time of using large-scale medical equipment in a hospital, then inputs the counted data into a host, then generates a chart by using the host, and divides the time periods of the peak time, the middle peak time and the low peak time of using the large-scale medical equipment; step two, equipment quantity statistics: medical staff count the number of large medical equipment used by the hospital, and then correspond the data to the peak time, the peak time and the peak time used by the large medical equipment in the step one, and the data are combined and then input into a host, and a chart is generated by using the host; step three, controlling time and quantity: connecting the large medical equipment with the terminal equipment, further controlling the starting time and the number of the started large medical equipment through the terminal equipment, and then counting the energy consumption data of the large medical equipment and inputting the energy consumption data into the host for storage; step four, equipment is added in an emergency mode: the large-scale medical equipment that will not open is connected with the artificial intelligence in the terminal equipment, meet the condition that needs promptly increase large-scale medical equipment, artificial intelligence starts the large-scale medical equipment quantity that needs to increase fast, make statistics of the live time and the live quantity of large-scale medical equipment through the hospital, then combine both data, utilize terminal equipment to carry out the time quantum to the live quantity of large-scale medical equipment and divide, reduce the quantity at the peak period and the low peak period of large-scale medical equipment use, large-scale medical equipment has been avoided working always and has consumed a large amount of electric quantities, the waste of the energy is very big less, the energy consumption of hospital has been saved.
(2) The large-scale medical equipment energy consumption prediction method based on artificial intelligence and the terminal equipment, by connecting the output end of the input module with the input end of the central processing system, connecting the output end of the central processing system with the input end of the time setting module, connecting the output end of the time setting module with the input end of the driving module, connecting the output end of the driving module with the input ends of the output module and the feedback module, connecting the output end of the feedback module with the input end of the central processing system, connecting the output end of the voice control unit with the input end of the driving module, the data of the using time and the using quantity of the large-scale medical equipment are input through the input module, and after the data are analyzed and processed through the central processing system, the time setting module is used for setting time values to start different quantities of large medical equipment, and the different quantities of large medical equipment are reasonably started according to the peak time, the middle peak time and the low peak time.
(3) This large-scale medical equipment energy consumption prediction method and terminal equipment based on artificial intelligence, through extracting the output of module and being connected with control chip's input, control chip's output and identification module's input are connected, identification module's output and data conversion module's input are connected, data conversion module's output and the input of proposing the module are connected, extract voice information through extracting the module, then control chip handles the back of analysis, and analyze through identification module, and then export information to drive module through data conversion module, meet the condition that needs the urgent large-scale medical equipment that increases, the large-scale medical equipment quantity that needs to increase can be started fast to the speech control unit, intelligent degree is high.
Drawings
FIG. 1 is a flow chart of a method for energy consumption prediction according to the present invention;
FIG. 2 is a schematic block diagram of a system of terminal devices according to the present invention;
FIG. 3 is a schematic block diagram of a voice control unit of the present invention.
In the figure, 1-input module, 2-central processing system, 3-time setting module, 4-driving module, 5-output module, 6-feedback module, 7-voice control unit, 71-extraction module, 72-control chip, 73-recognition module, 74-data conversion module and 75-extraction module.
Detailed Description
The technical solutions in the embodiments of the present invention will be 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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-3, an embodiment of the present invention provides a technical solution: the large-scale medical equipment energy consumption prediction method based on artificial intelligence specifically comprises the following steps:
step one, equipment time statistics: medical staff counts the time of using large medical equipment by a hospital, then the counted data is input into a host, then a chart is generated by using the host, the peak time, the middle peak time and the low peak time of using the large medical equipment are divided, in the step one, the time of using the large medical equipment by the hospital is counted, the period of time counting is one week, and then the average value is obtained;
step two, equipment quantity statistics: medical staff counts the number of large medical equipment used by a hospital, and then data are corresponding to the peak time, the peak time and the peak time of the large medical equipment used in the step one, the two data are combined and input into a host, a chart is generated by using the host, in the step two, the number of the large medical equipment used by the hospital is counted, the period of counting the number of the large medical equipment used by the hospital is one week, and then an average value is obtained;
step three, controlling time and quantity: connecting the large medical equipment with the terminal equipment, controlling the starting time and the starting number of the large medical equipment through the terminal equipment, counting the energy consumption data of the large medical equipment and inputting the energy consumption data into the host for storage, and calling and analyzing the data counted by the energy consumption data of the large medical equipment in the third step after being read by the chip at any time;
step four, equipment is added in an emergency mode: the large medical equipment which is not started is connected with the artificial intelligence in the terminal equipment, and the quantity of the large medical equipment which needs to be increased is quickly started by the artificial intelligence when the large medical equipment needs to be increased urgently.
The invention also discloses terminal equipment, which comprises an input module 1, a central processing system 2, a time setting module 3, a driving module 4, an output module 5, a feedback module 6 and a voice control unit 7, wherein the central processing system 2 is an ARM9 series microprocessor, the voice control unit 7 comprises an extraction module 71, a control chip 72, an identification module 73, a data conversion module 74 and a presentation module 75, the control chip 72 is an ARM9 series microprocessor, the output end of the identification module 73 is connected with the input end of the data conversion module 74, the output end of the data conversion module 74 is connected with the input end of the presentation module 75, the output end of the extraction module 71 is connected with the input end of the control chip 72, the output end of the control chip 72 is connected with the input end of the identification module 73, the output end of the feedback module 6 is connected with the input end of the central processing system 2, the output end of the voice control unit 7 is connected with, the output end of the time setting module 3 is connected with the input end of the driving module 4, the output ends of the driving module 4 are connected with the input ends of the output module 5 and the feedback module 6, the output end of the input module 1 is connected with the input end of the central processing system 2, and the output end of the central processing system 2 is connected with the input end of the time setting module 3.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. The large-scale medical equipment energy consumption prediction method based on artificial intelligence is characterized by comprising the following steps: the method specifically comprises the following steps:
step one, equipment time statistics: medical staff counts the time of using large-scale medical equipment in a hospital, then inputs the counted data into a host, then generates a chart by using the host, and divides the time periods of the peak time, the middle peak time and the low peak time of using the large-scale medical equipment;
step two, equipment quantity statistics: medical staff count the number of large medical equipment used by the hospital, and then correspond the data to the peak time, the peak time and the peak time used by the large medical equipment in the step one, and the data are combined and then input into a host, and a chart is generated by using the host;
step three, controlling time and quantity: connecting the large medical equipment with the terminal equipment, further controlling the starting time and the number of the started large medical equipment through the terminal equipment, and then counting the energy consumption data of the large medical equipment and inputting the energy consumption data into the host for storage;
step four, equipment is added in an emergency mode: connecting unopened large medical equipment with artificial intelligence in the terminal equipment, and rapidly starting the number of the large medical equipment needing to be increased by the artificial intelligence when the large medical equipment needs to be increased urgently;
in the first step, the time of using the large medical equipment by the hospital is counted, the time counting period is one week, and then an average value is obtained;
in the second step, the number of the large medical equipment used by the hospital is counted, the using and counting period of the number is one week, and then the average value is obtained;
the data after the statistics of the energy consumption data of the large and medium medical equipment in the third step can be read by the chip at any time and then called for analysis;
the terminal equipment comprises an input module (1), a central processing system (2), a time setting module (3), a driving module (4), an output module (5), a feedback module (6) and a voice control unit (7), wherein the output end of the input module (1) is connected with the input end of the central processing system (2), and the output end of the central processing system (2) is connected with the input end of the time setting module (3);
the voice control unit (7) comprises an extraction module (71), a control chip (72), a recognition module (73), a data conversion module (74) and a proposing module (75);
the voice information is extracted through the extraction module, then the control chip processes and analyzes the voice information, the voice information is analyzed through the recognition module, then the information is output to the driving module through the data conversion module, and when the condition that large medical equipment needs to be increased urgently is met, the voice control unit quickly starts the number of the large medical equipment needing to be increased.
2. The large medical device energy consumption prediction method based on artificial intelligence of claim 1, wherein: the output end of the time setting module (3) is connected with the input end of the driving module (4), and the output end of the driving module (4) is connected with the input ends of the output module (5) and the feedback module (6).
3. The large medical device energy consumption prediction method based on artificial intelligence of claim 1, wherein: the output end of the feedback module (6) is connected with the input end of the central processing system (2), and the output end of the voice control unit (7) is connected with the input end of the driving module (4).
4. The large medical device energy consumption prediction method based on artificial intelligence of claim 1, wherein: the output end of the extraction module (71) is connected with the input end of a control chip (72), and the output end of the control chip (72) is connected with the input end of an identification module (73).
5. The large medical device energy consumption prediction method based on artificial intelligence of claim 4, wherein: the output end of the identification module (73) is connected with the input end of the data conversion module (74), and the output end of the data conversion module (74) is connected with the input end of the extraction module (75).
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| CN107844859B (en) * | 2017-10-31 | 2020-08-07 | 深圳达实智能股份有限公司 | Large medical equipment energy consumption prediction method based on artificial intelligence and terminal equipment |
| CN208141207U (en) * | 2018-04-25 | 2018-11-23 | 北京新源绿网节能科技有限公司 | A kind of platform for the diagnosis of factory's efficiency |
| CN108958055A (en) * | 2018-07-30 | 2018-12-07 | 马鞍山问鼎网络科技有限公司 | A kind of home control system based on artificial intelligence |
| CN109801695A (en) * | 2018-12-20 | 2019-05-24 | 北京子歌人工智能科技有限公司 | A kind of Medical data acquisition device based on artificial intelligence |
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| CN104115077A (en) * | 2011-12-16 | 2014-10-22 | 施耐德电气美国股份有限公司 | Co-location electrical architecture |
| WO2013128468A2 (en) * | 2012-03-01 | 2013-09-06 | Tata Consultancy Services Limited | Method and system for efficient real time thermal management of a data center |
| CN107506889A (en) * | 2017-07-07 | 2017-12-22 | 珠海格力电器股份有限公司 | Multi-energy data processing method, device, energy source processor and energy processing system |
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