CN117406798A - Automatic flow control method and system for oxygenerator - Google Patents
Automatic flow control method and system for oxygenerator Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 40
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims abstract description 170
- 239000001301 oxygen Substances 0.000 claims abstract description 170
- 229910052760 oxygen Inorganic materials 0.000 claims abstract description 170
- 238000001514 detection method Methods 0.000 claims abstract description 59
- 238000012544 monitoring process Methods 0.000 claims abstract description 16
- 238000005516 engineering process Methods 0.000 claims description 14
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- 230000002159 abnormal effect Effects 0.000 claims description 7
- 230000008054 signal transmission Effects 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000007621 cluster analysis Methods 0.000 claims description 3
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- 230000010354 integration Effects 0.000 claims description 3
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- 238000001179 sorption measurement Methods 0.000 description 13
- 230000005540 biological transmission Effects 0.000 description 6
- 239000003638 chemical reducing agent Substances 0.000 description 6
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- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 4
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- 238000006467 substitution reaction Methods 0.000 description 2
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- 206010002091 Anaesthesia Diseases 0.000 description 1
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- 206010021143 Hypoxia Diseases 0.000 description 1
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- C—CHEMISTRY; METALLURGY
- C01—INORGANIC CHEMISTRY
- C01B—NON-METALLIC ELEMENTS; COMPOUNDS THEREOF; METALLOIDS OR COMPOUNDS THEREOF NOT COVERED BY SUBCLASS C01C
- C01B13/00—Oxygen; Ozone; Oxides or hydroxides in general
- C01B13/02—Preparation of oxygen
- C01B13/0229—Purification or separation processes
- C01B13/0248—Physical processing only
- C01B13/0259—Physical processing only by adsorption on solids
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- C—CHEMISTRY; METALLURGY
- C01—INORGANIC CHEMISTRY
- C01B—NON-METALLIC ELEMENTS; COMPOUNDS THEREOF; METALLOIDS OR COMPOUNDS THEREOF NOT COVERED BY SUBCLASS C01C
- C01B13/00—Oxygen; Ozone; Oxides or hydroxides in general
- C01B13/02—Preparation of oxygen
- C01B13/0229—Purification or separation processes
- C01B13/0248—Physical processing only
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D7/00—Control of flow
- G05D7/06—Control of flow characterised by the use of electric means
- G05D7/0617—Control of flow characterised by the use of electric means specially adapted for fluid materials
- G05D7/0629—Control of flow characterised by the use of electric means specially adapted for fluid materials characterised by the type of regulator means
- G05D7/0635—Control of flow characterised by the use of electric means specially adapted for fluid materials characterised by the type of regulator means by action on throttling means
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- C—CHEMISTRY; METALLURGY
- C01—INORGANIC CHEMISTRY
- C01B—NON-METALLIC ELEMENTS; COMPOUNDS THEREOF; METALLOIDS OR COMPOUNDS THEREOF NOT COVERED BY SUBCLASS C01C
- C01B2210/00—Purification or separation of specific gases
- C01B2210/0028—Separation of the specific gas from gas mixtures containing a minor amount of this specific gas
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Abstract
The application discloses an oxygenerator flow automatic control method and system, relates to the technical field of flow control, and comprises the following steps: deploying wireless sensors and flow detection sensors at different segment nodes of the oxygenerator; acquiring oxygen concentration data through a flow detection sensor, collecting the oxygen concentration data and storing the oxygen concentration data into an oxygen concentration database; judging whether the oxygen concentration data has deviation or not by detecting the collected oxygen concentration data in the oxygen concentration database; setting a flow value and an interval time value generated by an oxygenerator; and reading the current oxygen concentration data value at intervals, calculating a flow set value and a flow difference value of the read oxygen concentration data value, and setting different programs according to the flow automatic control difference value range of the flow oxygenerator so as to realize automatic control of the flow of the oxygenerator. The invention automatically controls the flow of the oxygenerator so as to facilitate the management and the monitoring of the oxygenerator.
Description
Technical Field
The application relates to the technical field of flow control, in particular to an automatic flow control method and system for an oxygenerator.
Background
From the physiological function of human body, the metabolism is not separated from oxygen, and when the cells of human body are lack of oxygen, the oxidation reaction for maintaining life cannot be performed, and a series of physiological functions and metabolic functions of the human body are disturbed. Oxygenerators have become indispensable auxiliary equipment, and are used in a large number not only in the medical industry but also in the steel industry.
With the gradual increase of patients with cardiovascular and cerebrovascular diseases coming from the aging society, the regular oxygen inhalation can reduce the disease occurrence frequency and plays an important role for the rehabilitation of the body. In addition, the number of pregnant women to be produced in China per year reaches more than 200 thousands of people, and the oxygen generator is an indispensable auxiliary device for increasing the blood oxygen content of the mother body, so that the development of the fetus is promoted, and the oxygen is required to be inhaled intermittently every day after 12 weeks of pregnancy. Almost all diseases are accompanied by oxygen deficiency, and oxygen inhalation is widely used as a clinical aid in hospitals, and is an essential factor in first aid and treatment in hospitals. Anesthesia machines, respirators, sickrooms, hyperbaric oxygen chambers, emergency rooms, even general wards and the like are likely to rescue or treat patients with oxygen continuously for 24 hours, which requires the oxygen generator to continuously supply medical oxygen with qualified pressure, flow and purity.
The invention provides an automatic control method and an automatic control system for flow of an oxygenerator, which can monitor the oxygen concentration and automatically control the flow of the oxygenerator according to the oxygen concentration so as to facilitate management and monitoring of the oxygenerator, and ensure that a user can use oxygen more safely and conveniently.
Disclosure of Invention
The application provides an automatic flow control method of an oxygenerator.
In order to achieve the above purpose, the present application adopts the following technical scheme:
in a first aspect, there is provided a method for automatically controlling flow of an oxygenerator, the method comprising the steps of:
step S1, deploying wireless sensors and flow detection sensors at different segment nodes of an oxygenerator;
s2, acquiring first oxygen concentration data through the flow detection sensor, and collecting and storing the first oxygen concentration data into an oxygen concentration database;
step S3, calculating a difference value between the first oxygen concentration data and an oxygen concentration threshold value preset in the oxygen concentration database by detecting the first oxygen concentration data in the oxygen concentration database, judging the difference value as fault information when the difference value is not in a preset difference value range, returning to step S2, and entering step S4 when the difference value is in the preset difference value range;
step S4, setting a flow set value and an interval time value corresponding to the difference value generated by the oxygenerator when the oxygen concentration data of the difference value is within a preset difference value range;
and S5, reading the current oxygen concentration data value at intervals, calculating the flow difference between the flow set value and the read oxygen concentration data value, and setting different programs according to different flow difference ranges to realize automatic control of the flow of the oxygenerator.
In one possible embodiment, the step S1 includes: constructing a wireless sensor by using a wireless communication technology, wherein the wireless communication technology comprises a ZigBee technology;
and building a wireless sensor network according to the wireless communication technology.
In one possible embodiment, the step S1 includes: and acquiring the first oxygen concentration data through the flow detection sensor, transmitting detection signals through the wireless sensor network, and collecting and storing the first oxygen concentration data to the oxygen concentration database after receiving the detection signals.
In one possible implementation manner, the step S3 includes: and setting an oxygen sample detection model in the oxygen concentration database, detecting the collected first oxygen concentration data by using the oxygen sample detection model, and analyzing whether the first oxygen concentration data to be detected is abnormal or not.
In one possible embodiment, the method for detecting the collected first oxygen concentration data using the oxygen sample detection model, and analyzing whether the first oxygen concentration data to be detected is abnormal further includes:
collecting the first oxygen concentration data to be detected in the oxygen concentration database, and obtaining corresponding characteristic indexes;
carrying out normalized encoding pretreatment on the oxygen concentration sample data;
performing cluster analysis on the preprocessed data, acquiring the oxygen concentration sample data within a preset difference range, performing imaging display, and taking network normal data as a model training set;
establishing an oxygen sample detection model based on a convolutional neural network, wherein the oxygen sample detection model comprises a trunk part network, the convolutional neural network and a fully-connected network;
training an oxygen sample detection model to obtain a trained oxygen sample detection model;
and acquiring oxygen concentration sample data to be predicted in real time, inputting the oxygen concentration sample data to be predicted into a trained oxygen sample detection model, and analyzing whether the oxygen concentration sample data to be detected is abnormal or not.
In one possible implementation, the backbone partial network, convolutional neural network and fully-connected network are connected in sequence from front to back;
the main part network, the convolutional neural network and the fully-connected network are sequentially connected from front to back;
the main part network comprises a convolution layer, a batch normalization layer and an activation layer which are sequentially connected;
extracting data characteristics corresponding to the oxygen concentration sample data within a preset difference range by using a convolution layer, processing the input data by using a batch normalization layer, adjusting intermediate output parameters, introducing nonlinear factors by using an activation layer, sequentially inputting the data after the first processing into a convolution neural network after the first processing by using the convolution layer and the batch normalization layer activation layer, and finally carrying out characteristic integration by using a full-connection layer; the characteristics integrated by the full connection layer are guided to learn by using the loss function; presetting relevant super parameters, achieving the aim of optimizing the network parameter weight by continuously iterating the attenuation loss value until the iteration times are equal to the maximum iteration times, and stopping training the model training set to obtain a trained oxygen sample detection model.
In one possible implementation manner, the step S5 includes:
the method comprises the steps of visually monitoring the oxygenerator in real time, and setting an alarm program and a delay anti-interference program.
In a second aspect, the invention also provides an automatic flow control system of the oxygenerator, which comprises a control module, a detection module, a wireless signal transmission module and a monitoring module, wherein:
the control module is used for deploying wireless sensors and flow detection sensors at different sectional nodes of the oxygenerator, reading current oxygen concentration data values at intervals, calculating flow difference values of flow set values and the read oxygen concentration data values, and setting different programs according to different flow difference ranges to realize automatic control of the flow of the oxygenerator;
the detection module is used for detecting the collected oxygen concentration data in the oxygen concentration database;
the wireless signal transmission module is used for acquiring oxygen concentration data through the flow detection sensor, collecting the oxygen concentration data and storing the oxygen concentration data into the oxygen concentration database;
the monitoring module is used for carrying out visual monitoring on the oxygenerator in real time and setting an alarm function and a delay anti-interference function.
In a third aspect, the present invention also provides an electronic device comprising a processor and a memory; the processor includes the automatic control system for flow rate of an oxygen generator according to the second aspect.
In a fourth aspect, the present invention also provides a computer-readable storage medium comprising instructions; the instructions, when executed on an electronic device as described in the third aspect, cause the electronic device to perform the method as described in the first aspect.
The invention provides an automatic control method and an automatic control system for flow of an oxygenerator, which can monitor the oxygen concentration and automatically control the flow of the oxygenerator according to the oxygen concentration so as to facilitate management and monitoring of the oxygenerator, and ensure that a user can use oxygen more safely and conveniently.
The invention selects ZigBee technology to construct a wireless sensor network, collects detection signals through the wireless sensor network, timely outputs control parameters, has an automatic control function, can exchange data with other network nodes, and has a network communication function, so that the automatic control of the flow of the oxygenerator is better carried out.
Drawings
FIG. 1 is a flow chart of an automatic control method for flow of an oxygenerator and an automatic control method for flow of an oxygenerator in a system according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an oxygenerator in a method and a system for automatically controlling flow of the oxygenerator according to an embodiment of the present application;
fig. 3 is a block diagram of an oxygen sample detection model in an automatic flow control method and system for an oxygenerator according to an embodiment of the present application.
Reference numerals illustrate:
10. a filter; 11. an air pump; 12. a first flowmeter; 13. a first pressure gauge; 14. a four-way valve; 15. a first adsorption tower; 16. a second adsorption tower; 17. a throttle valve; 18. a gas storage tank; 19. a second flowmeter; 20. a second pressure gauge; 21. an oxygen concentration meter; 22. a pressure reducing gauge; 23. a pressure reducing valve; 24. a pressure reducer.
Description of the embodiments
It should be noted that the terms "first," "second," and the like in the embodiments of the present application are used for distinguishing between the same type of feature, and not to be construed as indicating a relative importance, quantity, order, or the like.
The terms "exemplary" or "such as" and the like, as used in connection with embodiments of the present application, are intended to be exemplary, or descriptive. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
The terms "coupled" and "connected" in connection with embodiments of the present application are to be construed broadly, and may refer, for example, to a physical direct connection, or to an indirect connection via electronic devices, such as, for example, a connection via electrical resistance, inductance, capacitance, or other electronic devices.
Example 1
According to the automatic flow control method and system for the oxygenerator, as shown in fig. 1, wireless sensors and flow detection sensors are deployed at different sectional nodes of the oxygenerator; acquiring oxygen concentration data through a flow detection sensor, collecting the oxygen concentration data and storing the oxygen concentration data into an oxygen concentration database; the oxygen concentration data collected by detection is judged in an oxygen concentration database; setting a flow value and an interval time value generated by an oxygenerator; and reading the current oxygen concentration data value at intervals, calculating a flow set value and a flow difference value of the read oxygen concentration data value, and setting different programs according to the flow automatic control difference value range of the flow oxygenerator so as to realize automatic control of the flow of the oxygenerator. The invention automatically controls the flow of the oxygenerator so as to facilitate the management and the monitoring of the oxygenerator.
As shown in fig. 2, the oxygenerator mainly comprises a filter 10, an air pump 11, a first flowmeter 12, a first pressure gauge 13, a four-way valve 14, a first adsorption tower 15, a second adsorption tower 16, a throttle valve 17, an air storage tank 18, a second flowmeter 19, a second pressure gauge 20, an oxygen concentration meter 21, a pressure reducing meter 22, a pressure reducing valve 23 and a pressure reducer 24, wherein the air pump 11 mainly provides continuous air for the whole oxygenerator, and the air pumps 11 with different air supply amounts are selected according to the designed oxygen outlet flow requirements; the four-way valve 14 can be used for selecting different outlets, alternately enters the first adsorption tower 15 and the second adsorption tower 16 to finish the adsorption process, a throttle valve 17 and an air storage tank 18 are arranged between the first adsorption tower 15 and the second adsorption tower 16 to assist in adsorption, and a nitrogen discharge path is selected to perform desorption after adsorption is finished; the adsorption tower is the core of the oxygenerator, a special molecular sieve is arranged in the adsorption tower, and after compressed air enters the filter, the molecular sieve preferentially adsorbs nitrogen under a certain pressure due to the difference of molecular structures, so that oxygen is separated out; the air storage tank 18 is used for storing the prepared oxygen and has the function of stabilizing the pressure of the whole system; the pressure reducing valve 23 can control the flow rate of the output oxygen according to different opening apertures; the first flowmeter 12, the second flowmeter 19, the first pressure gauge 13, the second pressure gauge 20, the oxygen concentration gauge 21, the pressure reducer 22 and the pressure reducer 24 are used for monitoring all parameters in the gas circuit in real time, so that the oxygen production efficiency is improved, and the use risk is reduced.
Therefore, in order to realize automatic control of the flow of the oxygenerator, a wireless sensor network is firstly constructed by using a wireless communication technology, and the acquired detection signals are transmitted through the wireless sensor network.
Because of the limitation of the use condition of the oxygenerator, a transmission protocol with low power consumption and good ductility is required, and the inventor considers the transmission protocol in various transmission protocols, and discovers that the ZigBee technology is a more suitable wireless communication technology, because the ZigBee technology is an open globalization standard wireless transmission protocol and is specially designed for wireless transmission. The ZigBee has the characteristics of low cost, low power consumption, low duty ratio and low delay, prolongs the service life of a power supply battery of a product to the maximum extent, and is an ideal technical scheme for numerous industrial communications. The ZigBee module protocol also provides a 128-bit AES encryption mode, supports a Mesh self-organizing network, simultaneously, network nodes can be connected together through a plurality of paths in a wireless transmission mode, the ZigBee protocol IEEE 802.15.4 is selected, a main device can selectively keep a specific data frame long or short according to a device descriptor of a slave device, analysis and processing are carried out on a detection value, control parameters are timely output to an actuator circuit, and the ZigBee module protocol has an automatic control function and can carry out data exchange with other network nodes. A ZigBee technology is selected to construct a wireless sensor network, and detection signals are acquired through the wireless sensor network. The wireless sensor network is used for detecting, sensing and collecting various information of the environment of the node deployment area or the sensing object of interest of the observer in real time, processing the information and then transmitting the information in a wireless mode.
Example 2
In this embodiment, further optimization is performed on the basis of embodiment 1, in order to automatically control the flow rate of the oxygenerator, measurement is performed through a flow detection sensor, as shown in fig. 2, a first flow meter 12, a second flow meter 19 and an oxygen concentration meter 21 are selected to measure the flow rate value and the concentration value of the oxygenerator, when oxygen is diffused, the actual diffusion amount is generally fluctuated in a large range along with the pressure change of a pipe network, the first pressure meter 13, the second pressure meter 20, a pressure reducer 22 and a pressure reducer 24 assist in measuring and controlling the pressure, the flow meter and the oxygen concentration meter measure the flow rate value and the concentration value of the oxygenerator, in order to adjust and control according to the set value of the flow rate, then the flow rate value and the concentration value of the oxygenerator are measured, and compared with the manually set value, and if the two values are equal, the flow rate at the moment is indicated to reach the set requirement; if the two values are not equal, the controller sends out a signal to adjust the control circuit of the oxygenerator until the flow value is equal to the set value.
Other portions of this embodiment are the same as those of embodiment 1, and thus will not be described in detail.
Example 3
In this embodiment, the foregoing embodiment 1 or 2 is further optimized, and in the process of measuring the flow value and the concentration value of the oxygen generator, the actual diffusing amount is uncertain when oxygen diffuses, and generally fluctuates in a large range along with the pressure change of the pipe network, so that the change is frequent. The characteristic that the oxygen dispersion quantity is uncertain and has large variation requires that the flowmeter has a large measuring range ratio, and meanwhile, the measuring precision is required to be ensured in the whole measuring range, the measuring precision is required to be ensured in both high flow and low flow, and the measuring precision of the flowmeter and the oxygen concentration meter on the oxygen generator cannot be ensured, so that the embodiment proposes to use a neural network model to ensure accurate oxygen concentration data detection.
Example 4
The embodiment is further optimized based on any one of the above embodiments 1 to 3, as shown in fig. 3, in the embodiment, a neural network model is used to ensure accurate oxygen concentration data to be detected, firstly, oxygen concentration sample data to be detected is collected, and corresponding characteristic indexes are obtained; carrying out normalized encoding pretreatment on oxygen concentration sample data; performing cluster analysis on the preprocessed data, obtaining standard oxygen concentration sample data, performing imaging display, and taking network normal data as a model training set; establishing an oxygen sample detection model based on a convolutional neural network, wherein the oxygen sample detection model comprises a trunk part network, the convolutional neural network and a fully-connected network; training an oxygen sample detection model to obtain a trained oxygen sample detection model; and acquiring oxygen concentration sample data to be predicted in real time, inputting the oxygen concentration sample data to be predicted into a trained oxygen sample detection model, and analyzing whether the oxygen concentration sample data to be detected is abnormal or not.
Other portions of this embodiment are the same as any of embodiments 1 to 3 described above, and thus will not be described again.
Example 5
In the oxygen sample detection model, the main part network, the convolutional neural network and the fully-connected network are sequentially connected from front to back; the backbone part network comprises a convolution layer, a batch normalization layer and an activation layer which are connected in sequence.
Extracting characteristics of standard data of standard oxygen concentration sample data by using a convolution layer, processing the input data by using a batch normalization layer, adjusting intermediate output parameters, introducing nonlinear factors by using an activation layer, performing first processing on the convolution layer and the batch normalization layer activation layer, sequentially inputting the data after the first processing into a convolution neural network, and finally performing characteristic integration by using a full connection layer; the characteristics integrated by the full connection layer are guided to learn by using the loss function; presetting relevant super parameters, achieving the aim of optimizing the network parameter weight by continuously iterating the attenuation loss value until the iteration times are equal to the maximum iteration times, and stopping training the model training set to obtain a trained oxygen sample detection model.
Other portions of this embodiment are the same as any of embodiments 1 to 4 described above, and thus will not be described again.
Example 6
The embodiment is further optimized based on any one of the embodiments 1 to 5, the oxygen flow is designed, and the flow value and the interval time value corresponding to the first oxygen concentration flow value generated by the oxygenerator are set; the method comprises the steps of reading a current oxygen concentration data value at intervals, calculating a flow set value and a flow difference value of the read oxygen concentration data value, setting different programs according to different flow difference ranges, realizing automatic control of the flow of the oxygenerator, setting different programs according to different flow difference ranges, setting different modes corresponding to different programs, setting a certain flow value according to different modes, automatically adjusting the flow value after setting the flow value, reading the current flow value once at intervals (such as 1s, 2s, 5s and the like) and comparing the current flow value with the set value. Meanwhile, in order to prevent unnecessary adjustment actions of the vibration interference assembly, a delay anti-interference program is added, delay time (such as 1s, 2s, 5s and the like) is set, and an adjustment subprogram is started when the flow difference value exceeds 50 in the delay time; otherwise, the interference is regarded as not being treated. If the regulation subprogram is started, the disturbance prevention subprogram is ended, the regulation subprogram judges the range of the regulation level in advance, then regulates the flow once every interval time, and changes the flow evenly and slowly until reaching the expected value, thereby realizing the automatic control of the flow of the oxygenerator.
Example 7
The invention also provides an automatic control system of the flow of the oxygenerator, which is matched with the automatic control of the flow of the oxygenerator, and comprises a control module, a detection module, a wireless signal transmission module and a monitoring module, wherein:
the control module is used for deploying wireless sensors and flow detection sensors at different sectional nodes of the oxygenerator, reading current oxygen concentration data values at intervals, calculating flow difference values of flow set values and the read oxygen concentration data values, and setting different programs according to different flow difference ranges to realize automatic control of the flow of the oxygenerator;
the detection module is used for detecting the collected oxygen concentration data in the oxygen concentration database;
the wireless signal transmission module is used for acquiring oxygen concentration data through the flow detection sensor, collecting the oxygen concentration data and storing the oxygen concentration data into the oxygen concentration database;
the monitoring module is used for carrying out visual monitoring on the oxygenerator in real time, and setting an alarm program and a delay anti-interference program.
Other portions of this embodiment are the same as any of embodiments 1 to 6 described above, and thus will not be described again.
Example 8
The invention also provides an electronic device, which comprises a processor and a memory; the processor comprises the automatic flow control system of the oxygenerator described in the embodiment.
Example 9
The present invention also provides a computer-readable storage medium comprising instructions; when the instructions are executed on the electronic device described in the above embodiment, the electronic device is caused to perform the method described in the above embodiment. In the alternative, the computer readable storage medium may be a memory.
The processor referred to in the embodiments of the present application may be a chip. For example, it may be a field programmable gate array (field programmable gate array, FPGA), an application specific integrated chip (application specific integrated circuit, ASIC), a system on chip (SoC), a central processing unit (central processor unit, CPU), a network processor (network processor, NP), a digital signal processing circuit (digital signal processor, DSP), a microcontroller (micro controller unit, MCU), a programmable controller (programmable logic device, PLD) or other integrated chip.
The memory to which embodiments of the present application relate 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 EPROM (EEPROM), or a flash memory. The volatile memory may be random access memory (random access memory, RAM) 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 (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (SLDRAM), and direct memory bus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system, apparatus and module may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the above-described device embodiments are merely illustrative, e.g., the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple modules or components may be combined or integrated into another device, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with respect to each other may be through some interface, indirect coupling or communication connection of devices or modules, electrical, mechanical, or other form.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physically separate, i.e., may be located in one device, or may be distributed over multiple devices. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated in one device, or each module may exist alone physically, or two or more modules may be integrated in one device.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using a software program, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device including one or more servers, data centers, etc. that can be integrated with the medium. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), etc.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An automatic flow control method for an oxygenerator is characterized by comprising the following steps:
step S1, deploying wireless sensors and flow detection sensors at different segment nodes of an oxygenerator;
s2, acquiring first oxygen concentration data through the flow detection sensor, and collecting and storing the first oxygen concentration data into an oxygen concentration database;
step S3, calculating a difference value between the first oxygen concentration data and an oxygen concentration threshold value preset in the oxygen concentration database by detecting the first oxygen concentration data in the oxygen concentration database, judging the difference value as fault information when the difference value is not in a preset difference value range, returning to step S2, and entering step S4 when the difference value is in the preset difference value range;
step S4, setting a flow set value and an interval time value corresponding to the difference value generated by the oxygenerator when the oxygen concentration data of the difference value is within a preset difference value range;
and S5, reading the current oxygen concentration data value at intervals, calculating the flow difference between the flow set value and the read oxygen concentration data value, and setting different programs according to different flow difference ranges to realize automatic control of the flow of the oxygenerator.
2. The method according to claim 1, wherein the step S1 includes:
constructing a wireless sensor by using a wireless communication technology, wherein the wireless communication technology comprises a ZigBee technology;
and building a wireless sensor network according to the wireless communication technology.
3. The method according to claim 1, wherein the step S2 includes:
and acquiring the first oxygen concentration data through the flow detection sensor, transmitting detection signals through the wireless sensor network, and collecting and storing the first oxygen concentration data to the oxygen concentration database after receiving the detection signals.
4. The method according to claim 1, wherein the step S3 includes:
and setting an oxygen sample detection model in the oxygen concentration database, detecting the collected first oxygen concentration data by using the oxygen sample detection model, and analyzing whether the first oxygen concentration data to be detected is abnormal or not.
5. The method for automatically controlling the flow rate of an oxygen generator according to claim 4, wherein the method for analyzing whether the first oxygen concentration data to be detected is abnormal by detecting the collected first oxygen concentration data using the oxygen sample detection model further comprises:
collecting the first oxygen concentration data to be detected in the oxygen concentration database, and obtaining corresponding characteristic indexes;
carrying out normalized encoding pretreatment on the oxygen concentration sample data;
performing cluster analysis on the preprocessed data, acquiring the oxygen concentration sample data within a preset difference range, performing imaging display, and taking network normal data as a model training set;
establishing an oxygen sample detection model based on a convolutional neural network, wherein the oxygen sample detection model comprises a trunk part network, the convolutional neural network and a fully-connected network;
training an oxygen sample detection model to obtain a trained oxygen sample detection model;
and acquiring oxygen concentration sample data to be predicted in real time, inputting the oxygen concentration sample data to be predicted into a trained oxygen sample detection model, and analyzing whether the oxygen concentration sample data to be detected is abnormal or not.
6. The automatic flow control method of an oxygen generator according to claim 5, comprising:
the main part network, the convolutional neural network and the fully-connected network are sequentially connected from front to back;
the main part network comprises a convolution layer, a batch normalization layer and an activation layer which are sequentially connected;
extracting data characteristics corresponding to the oxygen concentration sample data within a preset difference range by using a convolution layer, processing the input data by using a batch normalization layer, adjusting intermediate output parameters, introducing nonlinear factors by using an activation layer, sequentially inputting the data after the first processing into a convolution neural network after the first processing by using the convolution layer and the batch normalization layer activation layer, and finally carrying out characteristic integration by using a full-connection layer; the characteristics integrated by the full connection layer are guided to learn by using the loss function; presetting relevant super parameters, achieving the aim of optimizing the network parameter weight by continuously iterating the attenuation loss value until the iteration times are equal to the maximum iteration times, and stopping training the model training set to obtain a trained oxygen sample detection model.
7. The method according to claim 1, wherein the step S5 includes:
the method comprises the steps of visually monitoring the oxygenerator in real time, and setting an alarm program and a delay anti-interference program.
8. The utility model provides an oxygenerator flow automatic control system which characterized in that, including control module, detection module, wireless signal transmission module and monitoring module, wherein:
the control module is used for deploying wireless sensors and flow detection sensors at different sectional nodes of the oxygenerator, reading current oxygen concentration data values at intervals, calculating flow difference values of flow set values and the read oxygen concentration data values, and setting different programs according to different flow difference ranges to realize automatic control of the flow of the oxygenerator;
the detection module is used for detecting the collected oxygen concentration data in the oxygen concentration database;
the wireless signal transmission module is used for acquiring oxygen concentration data through the flow detection sensor, collecting the oxygen concentration data and storing the oxygen concentration data into the oxygen concentration database;
the monitoring module is used for carrying out visual monitoring on the oxygenerator in real time, and setting an alarm program and a delay anti-interference program.
9. An electronic device comprising a processor and a memory; the processor is configured to operate the oxygenerator flow automatic control system of claim 8.
10. A computer-readable storage medium, the computer-readable storage medium comprising instructions; the instructions, when run on an electronic device as claimed in claim 9, cause the electronic device to perform the method as claimed in any one of claims 1-7.
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