CN210090999U - Compare interior outer wet poor controlling means of culvert of exhaust treatment device - Google Patents
Compare interior outer wet poor controlling means of culvert of exhaust treatment device Download PDFInfo
- Publication number
- CN210090999U CN210090999U CN201921307722.6U CN201921307722U CN210090999U CN 210090999 U CN210090999 U CN 210090999U CN 201921307722 U CN201921307722 U CN 201921307722U CN 210090999 U CN210090999 U CN 210090999U
- Authority
- CN
- China
- Prior art keywords
- neural network
- treatment device
- gas treatment
- culvert
- exhaust
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn - After Issue
Links
- 238000013528 artificial neural network Methods 0.000 claims abstract description 93
- 238000010606 normalization Methods 0.000 claims abstract description 22
- 239000007789 gas Substances 0.000 claims description 64
- 239000002912 waste gas Substances 0.000 claims description 21
- 238000013527 convolutional neural network Methods 0.000 claims description 12
- 238000010438 heat treatment Methods 0.000 claims description 6
- 238000007789 sealing Methods 0.000 claims description 6
- 238000000746 purification Methods 0.000 claims description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 4
- 230000000052 comparative effect Effects 0.000 claims 1
- 238000009833 condensation Methods 0.000 abstract description 5
- 230000005494 condensation Effects 0.000 abstract description 5
- 238000004140 cleaning Methods 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 abstract description 2
- 239000000779 smoke Substances 0.000 description 27
- 239000003570 air Substances 0.000 description 11
- 238000001514 detection method Methods 0.000 description 10
- 238000000034 method Methods 0.000 description 10
- 238000005265 energy consumption Methods 0.000 description 3
- 239000003344 environmental pollutant Substances 0.000 description 3
- 238000007500 overflow downdraw method Methods 0.000 description 3
- 231100000719 pollutant Toxicity 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 2
- 210000003746 feather Anatomy 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 229910000831 Steel Inorganic materials 0.000 description 1
- 238000003915 air pollution Methods 0.000 description 1
- 239000012080 ambient air Substances 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 239000004568 cement Substances 0.000 description 1
- 238000003889 chemical engineering Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 229920006395 saturated elastomer Polymers 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Images
Landscapes
- Treating Waste Gases (AREA)
- Chimneys And Flues (AREA)
Abstract
The utility model discloses a compare poor controlling means of wet culvert inside and outside exhaust treatment device belongs to the gas cleaning field, including exhaust treatment device and install at the inside square degree of depth hybrid neural network ware of exhaust treatment device, the one end fixed mounting of degree of depth hybrid neural network ware has horn type convolution neural network ware, the other end fixedly connected with of degree of depth hybrid neural network ware and convolution neural network ware symmetry and the opposite horn type degree of depth normalization neural network ware of opening direction. The utility model discloses an outer wet culvert sensor and interior wet culvert sensor on the exhaust treatment device gather in real time in step and compare the data set curve of the two, confirm the change range of the wet culvert of gaseous in the exhaust treatment device, realize the temperature control of economical and reasonable condensation and intensification technology, can monitor mill's chimney, thermal power emission white plume, control exhaust treatment device's white plume and discharge.
Description
Technical Field
The utility model relates to a device especially relates to a compare poor controlling means of wet culvert inside and outside exhaust treatment device belongs to the gas cleaning field.
Background
In recent years, China is always dedicated to protecting the environment, saving energy, reducing emission and reducing atmospheric pollution. Among them, the new edition "environmental air quality standard" and the emission standards aiming at the thermal power, steel, cement, chemical industry and non-electric coal-fired boiler industries are the most powerful policies for promoting the implementation of the smoke treatment project of each enterprise. The problem of the traditional waste gas emission in the industries such as thermal power, chemical engineering and the like is solved, however, due to the visual problem, people continuously complain about white smoke and the unpleasant feeling of the white smoke. The new problem of white smoke air pollution still puzzles many enterprises, with the implementation of ultralow emission of coal-fired power plants, particulate matters, SO2, NOx and other pollutants in the smoke are effectively controlled to different degrees, but because the temperature of the smoke is lower after wet desulphurization, white smoke plume which extends for hundreds of meters to kilometers is formed in the process that saturated wet smoke contacts with ambient air and is gradually cooled, SO that visual pollution is caused, and great troubles are caused to the life of surrounding residents; meanwhile, the high-humidity environment promotes the secondary conversion of the primary pollutants, a reverse-humidity layer is formed to hinder the further diffusion of the pollutants, the reverse-humidity layer is one of the sources of the local haze formation, and the traditional white smoke detection method mainly depends on manual observation or a sensor. However, due to limited human resources and high cost, the method based on manual observation cannot monitor white smoke quickly and effectively for a long time. On the other hand, smoke sensors based on relative humidity sampling are also likely to exhibit severe time lag due to environmental changes, while also failing to completely cover the detection area. In general, existing white smoke detection methods are difficult to meet.
SUMMERY OF THE UTILITY MODEL
The utility model discloses a main objective is in order to solve prior art not enough and provide a poor controlling means of wet culvert inside and outside comparing exhaust treatment device.
The purpose of the utility model can be achieved by adopting the following technical scheme:
a control device for comparing the internal and external wet content difference of an exhaust gas treatment device comprises the exhaust gas treatment device and a square deep mixing neural network device arranged in the exhaust gas treatment device, wherein one end of the deep mixing neural network device is fixedly provided with a horn-shaped convolution neural network device, the other end of the deep mixing neural network device is fixedly connected with a horn-shaped deep normalization neural network device which is symmetrical to the convolution neural network device and has opposite opening directions, a host which is used for determining the variation range of the gas wet content in the exhaust gas treatment device is arranged in the deep mixing neural network device, an internal wet content sensor is arranged at the top of the deep mixing neural network device, one side of the internal wet content sensor is provided with a temperature sensor, a heater is fixedly arranged above the deep mixing neural network device, and a condenser is fixedly arranged at one side of the deep mixing neural network device, the exhaust port is formed in the other side of the deep mixing neural network device, an outer wet culvert sensor is fixedly mounted at one end of the waste gas treatment device, a first air inlet used for air inlet of the deep normalization neural network device is formed in the lower portion of the outer wet culvert sensor, and a second air inlet used for air inlet of the convolution neural network device is formed in the other side of the waste gas treatment device.
Preferably, the exhaust port is located on the back of the exhaust gas treatment device, a purification net for purifying exhaust gas is fixedly installed inside the exhaust port, and a sealing cover matched with the exhaust port is fixedly connected to the top of the exhaust port.
Preferably, a first exhaust gas detector is arranged inside the convolutional neural network device, a second exhaust gas detector is fixedly mounted inside the deep normalization neural network device, and the convolutional neural network device, the deep normalization neural network device and the deep mixing neural network device are communicated.
Preferably, an operation panel is arranged on the front surface of the exhaust gas treatment device.
Preferably, the heater is internally provided with uniformly distributed heating wires.
Preferably, the other end of the condenser is provided with an air outlet, and circulating water pipes which are uniformly distributed are arranged in the condenser.
The utility model has the advantages of: according to the utility model discloses a compare poor controlling means of interior outer wet culvert of exhaust treatment device, gather and compare the data set curve of the two through outer wet culvert sensor and the real-time synchronization of interior wet culvert sensor on the exhaust treatment device, control white feather discharges, combine into the depth mixed neural network through the method of feature fusion with the convolution neural network based on jump connection of degree of depth normalization neural network and brand-new, the poor detection of interior outer wet culvert of exhaust treatment device that realizes, control method, confirm the fluctuation range of the interior gas wet culvert of exhaust treatment device, realize the temperature control of economical and reasonable condensation and intensification technology, can monitor mill's chimney, thermal power emission white plume, control exhaust treatment device's white plume discharges; the comparison exhaust gas treatment device internal and external wet culvert difference control device based on the deep mixing neural network is applied to an exhaust gas treatment system, parameters in a large number of neural networks are reduced, the identification accuracy is improved while the operation speed is improved, the generation and discharge processes of white smoke plume can be accurately controlled in real time, the discharge of the white smoke plume can be remarkably reduced, and meanwhile, the energy consumption is also reduced.
Drawings
Fig. 1 is a schematic front view of the overall structure of a preferred embodiment of a differential wet culvert control device inside and outside an exhaust gas treatment device according to the present invention;
fig. 2 is a schematic perspective view of the overall structure of a preferred embodiment of a differential wet culvert control device inside and outside an exhaust gas treatment device according to the present invention;
fig. 3 is a schematic view of the back of the overall structure of a preferred embodiment of a differential control device for comparing the internal and external wet contents of an exhaust gas treatment device according to the present invention.
In the figure: 1-waste gas treatment device, 2-operation panel, 3-external wet culvert sensor, 4-condenser, 5-circulating water pipe, 6-gas outlet, 7-deep normalization neural network device, 8-heater, 9-heating wire, 10-convolution neural network device, 11-internal wet culvert sensor, 12-deep mixing neural network device, 13-purification net, 14-gas outlet, 15-sealing cover, 16-first gas inlet, 17-second gas inlet, 18-first waste gas detector, 19-second waste gas detector, 20-host computer and 21-temperature sensor.
Detailed Description
In order to make the technical solutions of the present invention clearer and clearer for those skilled in the art, the present invention will be described in further detail with reference to the following embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.
The first embodiment is as follows:
as shown in fig. 1-3, the device for controlling the difference in the internal and external humidity of the exhaust gas treatment device according to the present embodiment includes an exhaust gas treatment device 1 and a square deep hybrid neural network 12 installed inside the exhaust gas treatment device 1, a trumpet-shaped convolutional neural network 10 is fixedly installed at one end of the deep hybrid neural network 12, a trumpet-shaped deep normalized neural network 7 symmetrical to the convolutional neural network 10 and having an opposite opening direction is fixedly connected to the other end of the deep hybrid neural network 12, a host 20 for determining the variation range of the wet content of the gas in the exhaust gas treatment device 1 is installed inside the deep hybrid neural network 12, an internal humidity content sensor 11 is installed at the top of the deep hybrid neural network 12, a temperature sensor 21 is installed at one side of the internal humidity content sensor 11, a heater 8 is fixedly installed above the deep hybrid neural network 12, a condenser 4 is fixedly arranged on one side of a deep mixing neural network device 12, an exhaust port 14 is arranged on the other side of the deep mixing neural network device 12, an external moisture content sensor 3 is fixedly arranged at one end of a waste gas treatment device 1, a first air inlet 16 for air inlet of a deep normalization neural network device 7 is arranged below the external moisture content sensor 3, a second air inlet 17 for air inlet of a convolution neural network device 10 is arranged on the other side of the waste gas treatment device 1, the external moisture content sensor 3 and the internal moisture content sensor 11 on the waste gas treatment device 1 synchronously acquire data set curves in real time and compare the data set curves to control white feather emission, the deep normalization neural network and a brand-new convolution neural network based on jump connection are combined into the deep mixing neural network by a characteristic fusion method, and the internal and external moisture content difference detection and control method of the waste gas treatment device is realized, determining the variation range of the wet content of the gas in the waste gas treatment device 1, realizing the temperature control of the economical and reasonable condensation and heating processes, monitoring white smoke plume discharged from a factory chimney and a thermal power plant, and controlling the white smoke plume discharge of the waste gas treatment device; the comparison exhaust gas treatment device internal and external wet culvert difference control device based on the deep mixing neural network is applied to an exhaust gas treatment system, parameters in a large number of neural networks are reduced, the identification accuracy is improved while the operation speed is improved, the generation and discharge processes of white smoke plume can be accurately controlled in real time, the discharge of the white smoke plume can be remarkably reduced, and meanwhile, the energy consumption is also reduced.
In the present embodiment, as shown in fig. 1 and fig. 2, the exhaust port 14 is located at the back of the exhaust gas treatment device 1, and a purification net 13 for purifying exhaust gas is fixedly installed inside the exhaust port 14, a sealing cover 15 adapted to the exhaust port 14 is fixedly connected to the top of the exhaust port 14, a first exhaust gas detector 18 is installed inside the convolutional neural network 10, a second exhaust gas detector 19 is fixedly installed inside the deep normalization neural network 7, the convolutional neural network 10, the deep normalization neural network 7 and the deep mixing neural network 12 are communicated with the front of the exhaust gas treatment device 1 and provided with an operation panel 2, the heater 8 is provided with uniformly distributed heating wires 9 inside, the other end of the condenser 4 is provided with an air outlet 6, the condenser 4 is provided with uniformly distributed circulating water pipes 5 inside, and the convolutional neural network 10 generates a convolutional neural network inside, the deep normalization neural network device 7 generates a deep normalization neural network inside, the deep mixing neural network device 12 generates a deep mixing neural network inside, the temperature sensor 21 detects the high ground of the internal temperature of the exhaust gas treatment device 1 and transmits the temperature information to the operation panel 2, the heater 8 and the condenser 4 are conveniently adjusted according to the real-time temperature, the sealing cover 15 is arranged to conveniently seal the exhaust port 14 and prevent the exhaust gas from leaking, the first exhaust gas detector 18 provides detection information for the convolution neural network, the second exhaust gas detector 19 provides detection information for the deep normalization neural network, and the two ends of the deep mixing neural network device 12 are provided with no baffle plate, so that the deep normalization neural network device 7, the deep mixing neural network device 12 and the convolution neural network device 10 are communicated.
A control device for comparing the internal and external wet content differences of an exhaust gas treatment device comprises the following steps:
step 1: synchronously acquiring data in real time through an external wet culvert sensor 3 and an internal wet culvert sensor 11 on the waste gas treatment device 1 and comparing the data;
step 2: combining the comparison data into a deep hybrid neural network through a characteristic fusion method by a deep normalization neural network and a convolutional neural network based on hop connection;
and step 3: the processing data is used for detecting and controlling the internal and external wet culvert difference of the waste gas processing device;
and 4, step 4: determining the variation range of the gas wet content in the waste gas treatment device 1, and realizing the temperature control of the economical and reasonable condensation and heating processes;
and 5: white smoke plume discharged by a factory chimney and a thermal power generation can be monitored, and the white smoke plume discharge of the waste gas treatment device is controlled.
The external wet content sensor 3 and the internal wet content sensor 11 on the waste gas treatment device 1 in the step 1 synchronously acquire and compare data to form a data historical change curve, so that generation of white smoke plume is convenient to control, the step 2 collects and unifies the compared data and carries out data enhancement algorithm processing, parameters of a deep mixing neural network are trained step by step and optimized, discharge of the white smoke plume is controlled, p data are collected every minute in the step 1, and the deep mixing neural network 120 is not less than p and is not less than a host 20.
To sum up, in this embodiment, according to the comparison internal and external moisture content difference control device of the exhaust gas treatment device of this embodiment, the external moisture content sensor 3 and the internal moisture content sensor 11 on the exhaust gas treatment device 1 synchronously acquire and compare data set curves of the two in real time, so as to control the emission of white smoke, and combine the deep normalized neural network and a brand-new convolutional neural network based on jump connection into a deep hybrid neural network by a feature fusion method, so as to implement the internal and external moisture content difference detection and control method of the exhaust gas treatment device, determine the variation range of the gas moisture content in the exhaust gas treatment device 1, implement the temperature control of the economical and reasonable condensation and temperature rise process, monitor the emission of white smoke from the factory chimney and the thermal power generation, and control the emission of white smoke from the exhaust gas treatment device; the comparison exhaust gas treatment device internal and external wet content difference control device based on the deep mixed neural network is applied to an exhaust gas treatment system, parameters in a large number of neural networks are reduced, the operation speed is improved, the identification accuracy is improved, the generation and discharge processes of white smoke plume can be accurately controlled in real time, the discharge of the white smoke plume can be obviously reduced, the energy consumption is reduced, the deep mixed neural network is generated inside the deep mixed neural network device 7, the deep mixed neural network is generated inside the deep mixed neural network device 12, the temperature sensor 21 detects the temperature height inside the exhaust gas treatment device 1, the temperature information is transmitted to the operation panel 2, the heater 8 and the condenser 4 are conveniently adjusted according to the real-time temperature, the sealing cover 15 is arranged to conveniently seal the exhaust port 14 and prevent exhaust gas from leaking, the first exhaust gas detector 18 provides detection information for the convolutional neural network, the second exhaust gas detector 19 provides detection information for the deep normalization neural network, and two ends of the deep mixing neural network device 12 are provided with no baffle plate, so that the deep normalization neural network device 7, the deep mixing neural network device 12 and the convolution neural network device 10 are communicated.
The above description is only a further embodiment of the present invention, but the scope of protection of the present invention is not limited thereto, and any person skilled in the art can replace or change the technical solution and the concept of the present invention within the scope of the present invention.
Claims (6)
1. The utility model provides a compare poor controlling means of interior outer wet culvert of exhaust-gas treatment device, its characterized in that, include exhaust-gas treatment device (1) and install in the inside square deep mixing neural network ware (12) of exhaust-gas treatment device (1), the one end fixed mounting of deep mixing neural network ware (12) has loudspeaker type convolution neural network ware (10), the other end fixedly connected with of deep mixing neural network ware (12) with loudspeaker type deep normalization neural network ware (7) of convolution neural network ware (10) symmetry and opening opposite direction, the inside of deep mixing neural network ware (12) is equipped with host computer (20) that is used for confirming the fluctuation range of the wet culvert of gas in exhaust-gas treatment device (1), the top of deep mixing neural network ware (12) is equipped with interior wet culvert sensor (11), one side of interior wet culvert sensor (11) is equipped with temperature sensor (21), the top fixed mounting of degree of depth mixing neural network ware (12) has heater (8), one side fixed mounting of degree of depth mixing neural network ware (12) has condenser (4), gas vent (14) have been seted up to the opposite side of degree of depth mixing neural network ware (12), the one end fixed mounting of exhaust treatment device (1) has outer wet sensor (3) of containing, the below of outer wet sensor (3) of containing is seted up and is used for degree of depth normalization neural network ware (7) first air inlet (16) that admits air, the opposite side of exhaust treatment device (1) is seted up and is used for second air inlet (17) that convolutional neural network ware (10) admits air.
2. A device for controlling the difference between the humidity inside and the humidity outside a waste gas treatment device according to claim 1, wherein the exhaust port (14) is located at the back of the waste gas treatment device (1), a purification net (13) for purifying waste gas is fixedly installed inside the exhaust port (14), and a sealing cover (15) corresponding to the exhaust port (14) is fixedly connected to the top of the exhaust port (14).
3. A comparison exhaust gas treatment device internal and external wet-culvert difference control device according to claim 1, wherein a first exhaust gas detector (18) is arranged inside the convolution neural network device (10), a second exhaust gas detector (19) is fixedly arranged inside the deep normalization neural network device (7), and the convolution neural network device (10), the deep normalization neural network device (7) and the deep mixing neural network device (12) are communicated with each other.
4. A device for controlling the difference in moisture content between the inside and the outside of an exhaust gas treatment device according to claim 1, wherein an operation panel (2) is provided on the front surface of the exhaust gas treatment device (1).
5. A comparative exhaust gas treatment device inside and outside moisture content difference control device according to claim 1, characterized in that the heater (8) is internally provided with uniformly distributed heating wires (9).
6. A device for controlling the difference between the moisture content inside and outside a waste gas treatment device according to claim 1, wherein the other end of the condenser (4) is provided with an air outlet (6), and the condenser (4) is internally provided with uniformly arranged circulating water pipes (5).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201921307722.6U CN210090999U (en) | 2019-08-13 | 2019-08-13 | Compare interior outer wet poor controlling means of culvert of exhaust treatment device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201921307722.6U CN210090999U (en) | 2019-08-13 | 2019-08-13 | Compare interior outer wet poor controlling means of culvert of exhaust treatment device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN210090999U true CN210090999U (en) | 2020-02-18 |
Family
ID=69486052
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201921307722.6U Withdrawn - After Issue CN210090999U (en) | 2019-08-13 | 2019-08-13 | Compare interior outer wet poor controlling means of culvert of exhaust treatment device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN210090999U (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110362138A (en) * | 2019-08-13 | 2019-10-22 | 广州市天工开物科技有限公司 | A kind of method that exogenous damp contains poor control device and controls white plume in comparison emission-control equipment |
-
2019
- 2019-08-13 CN CN201921307722.6U patent/CN210090999U/en not_active Withdrawn - After Issue
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110362138A (en) * | 2019-08-13 | 2019-10-22 | 广州市天工开物科技有限公司 | A kind of method that exogenous damp contains poor control device and controls white plume in comparison emission-control equipment |
| CN110362138B (en) * | 2019-08-13 | 2024-04-19 | 广州市天工开物科技有限公司 | Control device for comparing internal and external wet culvert differences of waste gas treatment device and method for controlling white smoke plume |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102568172A (en) | System for remotely carrying out on-line monitoring on discharge concentration of waste gas dust discharged by desulfurizer | |
| CN201936436U (en) | Desulphurization device waste-gas soot-dust emission concentration remote on-line monitoring system | |
| CN210090999U (en) | Compare interior outer wet poor controlling means of culvert of exhaust treatment device | |
| CN103961992B (en) | Air cleaning unit | |
| CN212548994U (en) | Tail gas treatment system of rotary kiln | |
| CN110362138B (en) | Control device for comparing internal and external wet culvert differences of waste gas treatment device and method for controlling white smoke plume | |
| CN206300939U (en) | Organic waste-gas purification monitoring system | |
| CN107246661A (en) | A kind of intelligent air purifier for smoke removing | |
| CN205164413U (en) | Intelligent purifier of underground space contaminated air | |
| CN205382977U (en) | A discharging equipment for treating motor vehicle exhaust gases | |
| CN210293992U (en) | Integrated oil smoke concentration online monitor | |
| CN208138869U (en) | A kind of gas sampling pipeline anti-clogging back-flushing structure | |
| CN214075965U (en) | Flue gas polymer degree of depth denitration detecting system | |
| CN205340425U (en) | Automatic outdoor haze purger of response wind direction | |
| CN210953808U (en) | Tail gas detection device and unit applied to oil gas recovery processing system | |
| CN204227501U (en) | A kind of dual probe oil smoke detection system | |
| CN210814425U (en) | Device for purifying waste gas and generating negative ions by artificial waterfall | |
| CN208389759U (en) | A kind of low-temperature flue gas ozone denitration high efficiency reactor | |
| CN112684757A (en) | Waste gas monitoring control system of thermal power plant | |
| CN108019768A (en) | A kind of two-stage type gas heater flue gas processing device | |
| CN206778703U (en) | A kind of electric cleaner with elemental mercury oxidation function | |
| CN211603080U (en) | Device for rapidly monitoring, evaluating and selecting wet smoke plume | |
| CN208660625U (en) | A kind of second level overtemperature prote system applied in pulsed bag-type dust equipment | |
| CN218358467U (en) | Energy-saving tunnel ventilation clarifier | |
| CN219434796U (en) | Unorganized emission control integrated system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| AV01 | Patent right actively abandoned | ||
| AV01 | Patent right actively abandoned | ||
| AV01 | Patent right actively abandoned |
Granted publication date: 20200218 Effective date of abandoning: 20240419 |
|
| AV01 | Patent right actively abandoned |
Granted publication date: 20200218 Effective date of abandoning: 20240419 |