CN113834523A - Intelligent marine ranch culture system based on unmanned ship - Google Patents
Intelligent marine ranch culture system based on unmanned ship Download PDFInfo
- Publication number
- CN113834523A CN113834523A CN202111036313.9A CN202111036313A CN113834523A CN 113834523 A CN113834523 A CN 113834523A CN 202111036313 A CN202111036313 A CN 202111036313A CN 113834523 A CN113834523 A CN 113834523A
- Authority
- CN
- China
- Prior art keywords
- unmanned ship
- water quality
- marine ranch
- module
- data
- 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.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/70—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in livestock or poultry
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to an unmanned ship-based intelligent culture system for a marine ranching, which solves the technical problem that how to use an unmanned ship to detect and manage the marine ranching environment needs to be solved urgently by technical personnel in the field, and the working process is as follows: (1) setting operation targets such as a working area, a return position and the like of the unmanned ship through a ground station and control software, and releasing the unmanned ship to a target sea area; (2) after the unmanned ship reaches the target sea area, the shipborne unmanned ship autonomous controller automatically plans a path for operation, and starts a shipborne environment detection module to collect and detect fishery water quality indexes. Meanwhile, the shipborne communication module transmits all environmental indexes of the target sea area back to the ground station in real time. When the shipborne environment detection module detects that a certain fishery water quality index in the target sea area is abnormal, starting the shipborne water quality maintaining device to improve the water quality of the target sea area; (3) after the unmanned ship finishes the operation, the unmanned ship returns to and maintains the preset return position, and the staff can go to the preset position to be recovered.
Description
Technical Field
The invention relates to the field of analysis and measurement control, in particular to an intelligent culture system for a marine ranch based on an unmanned ship.
Background
With the increasing of the fishing strength, the ocean pollution range is continuously expanded, the decline phenomenon of the ocean fishery resources in China is increasingly serious, and the mariculture industry is rapidly developed in recent years as a supplement to ocean fishing. However, the problems of environmental, disease and quality safety brought by mariculture are increasingly highlighted, and resources and environment in fishery development and a series of problems brought by the resources and the environment become one of bottlenecks which restrict sustainable development of marine aquaculture industry and marine fishery in China. Research, development and application dedicated to marine ranches become strategic choices of main marine countries, are one of the main attacking directions of fishery development of developed countries in the world, and are worthy of close attention and research in China.
In which the deep and continuous research on marine ecological monitoring and biological resource management is essential for the sustainable development of marine ranches. The monitoring capability construction comprises the monitoring of the quality of the ecological environment and the monitoring of biological resources, the protection and treatment of the water quality environment of the marine ranch are enhanced, and the construction is an important guarantee for maintaining the sustainable development of the marine ranch industry. However, fishery monitoring work is difficult to frequently monitor due to various factors such as large workload, wide working range and the like, so that relevant monitoring data of fishery sea water quality conditions cannot be updated in time. At present, domestic environment monitoring ships are still few, and the following two types of products are mainly represented by the same type: (1): the "astronomical phenomena one" unmanned sea exploration ship. The astronomical phenomena one unmanned ship meteorological detection system can provide measured values of wind speed, wind direction, air temperature, humidity, water temperature and the like. The main operation object is meteorological conditions. "planetarium one" makes a great contribution to the weather guarantee service of the Qingdao Osai of the Beijing Olympic Games, which is the first time unmanned ship is applied to weather detection in the world. (2) "Changjiang river water conservation supervision 2000". The monitoring ship (large-scale manual driving) can quickly and accurately monitor the water environment of the Yangtze river, supervise and monitor the pollution discharge condition of the river entering along the river, and dynamically monitor water bodies in provinces, key polluted river reach and water pollution accidents. The ship is put into use since 2000, and is specially provided with a plurality of sets of professional equipment before the 'Yangtze river action' is started.
At present, the research on the marine ranching environment detection technology is mainly embodied as follows: the system comprises an automatic water quality monitoring system, an online automatic smoke and dust and SO2 monitoring system, an automatic continuous organic pollutant monitoring system, a heavy pollution source monitoring system, an online portable emergency monitoring device and the like.
Therefore, how to apply the unmanned ship to detect and manage the marine ranch environment is a technical problem to be solved urgently by those skilled in the art.
Disclosure of Invention
The invention provides an unmanned ship-based intelligent culture system for a marine ranching, aiming at solving the technical problem that how to detect and manage the marine ranching environment by using an unmanned ship is urgently needed to be solved by technical personnel in the field.
The invention discloses an unmanned ship-based intelligent marine ranch culture system which comprises a ground station, an unmanned ship, an environment detection module, a sensor fusion sensing module, an adaptive intelligent decision module and a water quality maintaining device, wherein the unmanned ship is provided with an unmanned ship autonomous controller, a communication module and a Beidou navigation device; the environment detection module, the sensor fusion sensing module, the adaptive intelligent decision-making module and the water quality maintaining device are respectively arranged on the unmanned ship;
the environment detection module is configured to continuously track and monitor the marine ranch environment in real time and detect various fishery water quality parameters;
the sensor fusion sensing module is configured to perform fusion processing analysis on the water quality parameter data detected by the environment detection module; the data after fusion processing and analysis are transmitted to the ground station through the communication module;
the ground station is configured to set an unmanned ship working area, a return location, and release the unmanned ship to a target sea area; receiving data subjected to fusion processing and analysis by the sensor fusion sensing module, judging and analyzing the real-time state of the marine ranch, and judging whether the water quality parameters are in a normal range;
the water quality maintaining device is configured to be started to improve the water quality when the ground station judges that the water quality parameter is not in a normal range;
the unmanned ship autonomous controller is configured to control the unmanned ship to operate in a data-driven mode according to data information output by the sensor fusion sensing module and information of multiple time and space scales after the unmanned ship reaches a target sea area; and automatically planning a path to enable the unmanned ship to perform autonomous operation.
Preferably, the unmanned ship-based intelligent culture system for the marine ranch further comprises an adaptive intelligent decision-making module;
the adaptive intelligent decision module is configured to make predictions of marine ranch conditions from real-time marine ranch condition data and multi-spatio-temporal scale data.
The invention has the advantages that the real-time monitoring of the marine ranch environment can be realized through the application of technologies such as autonomous navigation, environment detection, water quality maintenance and the like, the strong marine habitat monitoring and analyzing capability is formed, the automatic and intelligent construction of the marine ranch is promoted, and the linked economic benefit can be brought.
Compared with a manual driving monitoring ship, the unmanned ship can more accurately reach a monitored water area, and human resources are saved.
Can monitor various fishery water quality parameters and can realize real-time continuous tracking and monitoring of the marine ranch environment.
And (3) autonomous detection of water quality indexes: the shipborne environment detection device can judge whether the water quality parameters are in a normal range. When the water quality parameters are not in the normal range, the shipborne water quality maintaining device is started, the water quality is automatically improved, and the intelligence of the ship is improved.
Clean energy power system: the unmanned ship carries a solar power supply system, and zero pollution discharge of the unmanned ship is realized.
A perfect security system: the unmanned ship carries various sensors (such as infrared pyroelectric sensors and water inlet sensors), and can give an alarm in time when meeting emergency conditions such as outsider invasion, obstacles, collision and the like, so that the unmanned ship is safe and reliable in navigation operation.
The integration level is high: the environment monitoring system is organically integrated with mature technologies such as GPS and the like, so that a plurality of problems of system integration are solved, and the advantages of a single technology are converted into the advantages of an application system. Compared with other monitoring ships, the ship has small volume and convenient operation.
Further features and aspects of the present invention will become apparent from the following description of specific embodiments with reference to the accompanying drawings.
Drawings
FIG. 1 is a schematic structural view of the unmanned ship of the present invention;
FIG. 2 is a schematic diagram of an unmanned ship provided with an environment detection module, an adaptive intelligent decision-making module, a sensor fusion sensing module and a water quality maintaining device;
fig. 3 is a system schematic.
The symbols in the drawings illustrate that:
1. the system comprises an unmanned ship, 1-2 unmanned ship autonomous controllers, 1-3 communication modules, 1-4 Beidou navigation devices, 2 environment detection modules, 3 water quality maintaining devices, 4 adaptive intelligent decision-making modules and 5 sensor fusion sensing modules.
Detailed Description
The present invention will be described in further detail below with reference to specific embodiments thereof with reference to the attached drawings.
As shown in figures 1 and 2, the unmanned ship and various sensors are used as physical bases, and the unmanned ship 1 is provided with an unmanned ship autonomous controller 1-2, a communication module 1-3 and a Beidou navigation device 1-4. The environment detection module 2, the sensor fusion sensing module 5, the adaptive intelligent decision module 4 and the water quality maintenance device 3 are respectively installed on the hull of the unmanned ship 1.
The invention discloses an intelligent detection and culture method for a marine ranch based on an unmanned ship, which is mainly carried out through the following processes: (1) setting operation targets such as a working area, a return position and the like of the unmanned ship through a ground station and control software, and releasing the unmanned ship to a target sea area; (2) after the unmanned ship reaches the target sea area, the shipborne unmanned ship autonomous controller automatically plans a path for operation, and starts a shipborne environment detection module to collect and detect fishery water quality indexes. Meanwhile, the shipborne communication module transmits all environmental indexes of the target sea area back to the ground station in real time. When the shipborne environment detection module detects that a certain fishery water quality index in the target sea area is abnormal, starting the shipborne water quality maintaining device to improve the water quality of the target sea area; (3) after the unmanned ship finishes the operation, the unmanned ship returns to and maintains the preset return position, and a worker can go to the preset position to recover the unmanned ship or remotely control the unmanned ship to sail to the specified position and recover the unmanned ship through a ground station and control software.
The environment detection module 2 integrates a batch of environment monitoring sensors, can realize real-time continuous tracking and monitoring of the marine ranch environment, and can detect various water quality parameters of fishery water, such as water temperature, pH value, dissolved oxygen, heavy metal content, ammonia nitrogen and the like.
The sensor fusion sensing module 5 is mainly used for calculating and processing the water quality parameter data detected by the environment detection module 2, mining deep-level relation among different sensor data, carrying out fusion processing analysis on the data, transmitting the data subjected to fusion processing analysis to the ground station in a wireless mode through the communication modules 1-3, and judging and analyzing the real-time state of the marine ranch by software of the ground station to judge whether the water quality parameter is within a normal range. When the water quality parameter is not in the normal range, the water quality maintaining device 3 is started to improve the water quality and maintain the water quality in the normal range. (for example, when the oxygen content of the water body is detected to be insufficient, the unmanned ship starts the shipborne oxygen increasing machine so as to improve and maintain the oxygen content of the water body in the target sea area to be within a normal range.
Control software on the ground station sets operation targets such as the unmanned ship working area and the return position, and releases the unmanned ship to a target sea area. After the unmanned ship arrives at the target sea area, the unmanned ship autonomous controller 1-2 can control the operation of the unmanned ship in a data-driven mode according to data information output by the sensor fusion sensing module 5 and information of multiple time and space scales. The specific analysis process for controlling the motion of the unmanned ship in a data-driven mode comprises the following steps: the method is characterized in that firstly, the time-space synchronization cooperativity of multi-source sensor data is realized, time alignment is mainly realized by designing a timestamp in a mode of combining software and hardware triggering, after hardware synchronization triggering signals are sent on the basis of remote communication, the inside of a system realizes the periodic effectiveness of levels through programming so as to realize simultaneous triggering, and data collected by sensors with different sampling frequencies are optimized in a least square method so as to realize the time cooperativity. The spatial cooperativity is mainly to realize the same coordinate system calibration among different sensor data through spatial coordinate conversion, for example, the real coordinates of image data are obtained through solving an internal parameter matrix and an external parameter matrix of camera imaging, so that the same airspace contrast analysis with radar data is realized. Secondly, preprocessing multi-source sensor data, and mining deep internal relation among different sensors, for example, information such as types of targets and the like can be obtained through simple image processing, and actual three-dimensional contour information and the like of the target can be better iterated on the basis of radar and image data through a Bayesian network by combining prior knowledge of the target types. The last part is fusion processing based on multi-source sensor data, the part takes multiple times of sensor acquisition data and corresponding marine environment states as training bases, training optimization is carried out on a fusion model through a deep learning neural network, and therefore analysis and judgment can be carried out on the multi-source data, and continuous learning optimization is carried out on the basis that an incremental learning network is added in practical application and new acquisition data are analyzed.
The unmanned ship autonomous controller 1-2 can automatically plan a path to enable the unmanned ship to perform autonomous operation, the autonomous path planning is based on the communication module 1-3 and the Beidou navigation device 1-4, and the specific implementation process is as follows:
taking a marine ranch as a communication node n, and n is the communication node n for a multi-marine ranch network1,n2,n3…..nmCorresponding to the marine ranch 1, marine ranch 2, marine ranch 3, up to the marine ranch m, and, in addition, assume n0Is a port node.
And constructing a path type communication network according to the distance information of the plurality of marine ranch nodes and the port node. With port node n0As an initial node P of the communication path0Then, the kth communication path node is obtained by the following formula:
Pk＝nmin(d(nk,nk+1),d(nk,nk+2),d(nk,nk+3),...d(nk,nm))
in the above formula, d (n)k,nk+1) Indicating the distance between the kth node and the (k + 1) th node. And selecting the next node of which the shortest distance forms the communication path network by traversing the distance value between the subsequent node and the node at each node position so as to construct the whole shortest communication path network structure.
After the communication path network is constructed, each node collects and senses the state and the environmental information of the marine ranch through the multi-source sensor, the acquired information is transmitted to the preceding-stage node through the communication network, and the like, and the information of all the marine ranches is transmitted back to the port step by step through the communication path network for comprehensive analysis and judgment.
After the analysis and judgment are completed, the information of each node is subjected to the big data analysis described in the above section, and the state of each marine ranch is analyzed, for example, the oxygen content replenishment state of the water quality of the marine ranch is recorded as SoSuppose SoIs a real number between 0 and 1, the larger the value is, the more oxygen supplementation is needed for the water quality in the corresponding marine ranch, and the So(Pn) And showing the water quality and oxygen content supply condition of the marine ranch at the nth node of the communication path. The autonomous navigation path of the unmanned ship is completed by the following formula:
in the above formula, Ua(t) represents the magnitude of the potential force at the t position in the automatic path planning, the greater the potential force, the closer the t position is to the end point of the path planning, eta is the potential force coefficient, So,goalIndicating the water and oxygen content replenishment state of the target site, So(Pt) And p represents absolute value information of a distance difference value, and represents an absolute value of a difference value between the distance between the initial node and the target node and the distance between the initial node and the current nearest node.
By the gravitation function UaAnd (t) taking the communication path network as a pre-planned path, and calculating the magnitude of a relevant potential force field value in the path, so that a potential difference can be formed, the potential force value of a target position is the largest, and the unmanned ship traverses the whole communication path according to the magnitude of the potential force value, so that the unmanned ship can quickly and efficiently reach a destination to supply the water quality and the oxygen content.
Similarly, the state diagnosis and replenishment of other marine ranches are basically consistent with the unmanned ship path rule algorithm of the water quality and oxygen content, and the state function is replaced only in the process of solving the potential force function. The characterization range and meaning for different state functions may be different, so normalization processing is required before use.
The adaptive intelligent decision module 4 is based on a marine ranch state detection system, under the condition of detecting the marine ranch state in real time, the marine ranch state is predicted according to real-time marine ranch state data and multi-space-time scale data, and the specific implementation process is as follows:
(1) and analyzing the state of the marine ranch. The environmental information of the marine ranch is collected and analyzed through the environmental detection module 2 and the sensor fusion sensing module 5, and the three-dimensional environment and state of the marine ranch are analyzed by means of the deep learning neural network, wherein the three-dimensional environment and state comprise information such as the change of the surrounding environment of the marine ranch, the self water quality analysis and the like.
(2) And predicting the state trend of the marine ranch. Optimizing and predicting the state trend of the marine ranch by a basic network framework based on a convolution type long-short term memory network (ConvLSTM), analyzing the trend item, the approach inertia, the periodicity and the spatial correlation of a marine ranch real-time space-time data sequence, and introducing a periodic network, a trend network and a space-time attention module to capture the short-term signal and the long-term trend item in the marine ranch real-time space-time sequence data. Extracting short-term trend characteristics by using a circulating network, fusing historical data characteristics transmitted by the circulating network, introducing a space-time attention mechanism into the network, cascading time attention and space attention, and calculating the similarity of the periodic trend and the adjacent trend so as to obtain marine ranching state trend prediction.
(3) Intelligent decision making based on predicted trends. By introducing an intelligent expert system, namely collecting and arranging corresponding processing methods of different state problems of the marine ranch in advance, training and optimizing the expert system through a neural network, on the basis of analyzing and predicting the state of the marine ranch, a prediction result is led into the intelligent expert system, and an intelligent analysis decision is made according to the predicted state trend of the marine ranch.
By adopting the whole set of intelligent detection method for the marine ranching based on unmanned ship autonomous path planning, the whole process is highly intelligent, so that the culture efficiency of the marine ranching can be effectively improved; the mode of combining the real-time data and the multi-space-time scale data analysis is adopted, the marine ranch state environment is effectively predicted, and partial natural disasters can be effectively avoided.
The above description is only for the purpose of illustrating preferred embodiments of the present invention and is not to be construed as limiting the present invention, and it is apparent to those skilled in the art that various modifications and variations can be made in the present invention. All changes, equivalents, modifications and the like which come within the scope of the invention as defined by the appended claims are intended to be embraced therein.
Claims (2)
1. An intelligent marine ranch culture system based on an unmanned ship is characterized by comprising a ground station, the unmanned ship, an environment detection module, a sensor fusion sensing module, an adaptive intelligent decision-making module and a water quality maintaining device, wherein the unmanned ship is provided with an unmanned ship autonomous controller, a communication module and a Beidou navigation device; the environment detection module, the sensor fusion sensing module, the adaptive intelligent decision-making module and the water quality maintaining device are respectively arranged on the unmanned ship;
the environment detection module is configured to continuously track and monitor the marine ranch environment in real time and detect various fishery water quality parameters;
the sensor fusion sensing module is configured to perform fusion processing analysis on the water quality parameter data detected by the environment detection module; the data after fusion processing and analysis are transmitted to the ground station through the communication module;
the ground station is configured to set an unmanned ship working area, a return location, and release the unmanned ship to a target sea area; receiving data subjected to fusion processing and analysis by the sensor fusion sensing module, judging and analyzing the real-time state of the marine ranch, and judging whether the water quality parameters are in a normal range;
the water quality maintaining device is configured to be started to improve the water quality when the ground station judges that the water quality parameter is not in a normal range;
the unmanned ship autonomous controller is configured to control the unmanned ship to operate in a data-driven mode according to data information output by the sensor fusion sensing module and information of multiple time and space scales after the unmanned ship reaches a target sea area; and automatically planning a path to enable the unmanned ship to perform autonomous operation.
2. The unmanned-vessel-based marine ranch intelligent farming system of claim 1, wherein the unmanned-vessel-based marine ranch intelligent farming system further comprises an adaptive intelligent decision making module;
the adaptive intelligent decision module is configured to make predictions of marine ranch conditions from real-time marine ranch condition data and multi-spatio-temporal scale data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111036313.9A CN113834523B (en) | 2021-09-06 | 2021-09-06 | Marine pasture intelligent breeding system based on unmanned ship |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111036313.9A CN113834523B (en) | 2021-09-06 | 2021-09-06 | Marine pasture intelligent breeding system based on unmanned ship |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113834523A true CN113834523A (en) | 2021-12-24 |
| CN113834523B CN113834523B (en) | 2023-07-11 |
Family
ID=78962189
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111036313.9A Active CN113834523B (en) | 2021-09-06 | 2021-09-06 | Marine pasture intelligent breeding system based on unmanned ship |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113834523B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108303508A (en) * | 2018-02-06 | 2018-07-20 | 武汉理工大学 | Ecology language system and method based on laser radar and deep learning optimum path search |
| CN110146674A (en) * | 2019-05-24 | 2019-08-20 | 广东交通职业技术学院 | A kind of intellectual monitoring unmanned boat |
| CN110723257A (en) * | 2019-10-15 | 2020-01-24 | 徐玲 | Unmanned ship for aquaculture |
| CN110850873A (en) * | 2019-10-31 | 2020-02-28 | 五邑大学 | Unmanned ship path planning method, device, equipment and storage medium |
| KR102115294B1 (en) * | 2020-02-28 | 2020-06-02 | 주식회사 파블로항공 | Collision Avoidance for UAV |
| CN211198830U (en) * | 2019-07-15 | 2020-08-07 | 浙江创韵环境科技有限公司 | Riverway restoration system |
| CN111882138A (en) * | 2020-08-07 | 2020-11-03 | 中国农业大学 | Water quality prediction method, device, equipment and storage medium based on space-time fusion |
| CN112269376A (en) * | 2020-09-14 | 2021-01-26 | 江苏大学 | Operation path planning and control method of movable aerator |
| CN112631293A (en) * | 2020-12-16 | 2021-04-09 | 江苏大学 | Unmanned ship anti-collision Internet of things control system and method based on artificial potential field method |
| CN113110439A (en) * | 2021-04-08 | 2021-07-13 | 江苏大学 | Real-time anti-storm unmanned ship air route control method and water quality monitoring system thereof |
-
2021
- 2021-09-06 CN CN202111036313.9A patent/CN113834523B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108303508A (en) * | 2018-02-06 | 2018-07-20 | 武汉理工大学 | Ecology language system and method based on laser radar and deep learning optimum path search |
| CN110146674A (en) * | 2019-05-24 | 2019-08-20 | 广东交通职业技术学院 | A kind of intellectual monitoring unmanned boat |
| CN211198830U (en) * | 2019-07-15 | 2020-08-07 | 浙江创韵环境科技有限公司 | Riverway restoration system |
| CN110723257A (en) * | 2019-10-15 | 2020-01-24 | 徐玲 | Unmanned ship for aquaculture |
| CN110850873A (en) * | 2019-10-31 | 2020-02-28 | 五邑大学 | Unmanned ship path planning method, device, equipment and storage medium |
| KR102115294B1 (en) * | 2020-02-28 | 2020-06-02 | 주식회사 파블로항공 | Collision Avoidance for UAV |
| CN111882138A (en) * | 2020-08-07 | 2020-11-03 | 中国农业大学 | Water quality prediction method, device, equipment and storage medium based on space-time fusion |
| CN112269376A (en) * | 2020-09-14 | 2021-01-26 | 江苏大学 | Operation path planning and control method of movable aerator |
| CN112631293A (en) * | 2020-12-16 | 2021-04-09 | 江苏大学 | Unmanned ship anti-collision Internet of things control system and method based on artificial potential field method |
| CN113110439A (en) * | 2021-04-08 | 2021-07-13 | 江苏大学 | Real-time anti-storm unmanned ship air route control method and water quality monitoring system thereof |
Non-Patent Citations (1)
| Title |
|---|
| 陈会伟 等: "基于人工势场法的无人船航迹规划研究现状分析", 科学技术创新, no. 17, pages 28 - 30 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113834523B (en) | 2023-07-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112422783B (en) | Unmanned aerial vehicle intelligent patrol system based on parking apron cluster | |
| CN104571099B (en) | Photovoltaic fault diagnosis system and method based on theoretical calculation and data analysis | |
| CN111516808B (en) | Environment monitoring river patrol robot system and method | |
| CN105123554B (en) | A kind of multi-function animal intelligence necklace | |
| CN110321853A (en) | Distribution cable external force damage prevention system based on video intelligent detection | |
| CN109752300A (en) | A kind of coating material production safe and intelligent crusing robot, system and method | |
| KR20160072432A (en) | System for providing observation data for marine environment | |
| CN116625327B (en) | Big data driven marine environment monitoring and early warning method | |
| CN108540310B (en) | Behavior prediction method based on wireless network cooperative sensing | |
| CN111735922B (en) | Aquaculture monitoring system based on underwater robot | |
| CN114169638A (en) | Water quality prediction method and device | |
| CN110163184A (en) | Intelligent video monitoring system and method | |
| CN103913721A (en) | Intelligent indoor personnel perceiving method based on artificial neural network | |
| Ashwini et al. | Intelligent model for predicting water quality | |
| CN116929454A (en) | River water pollution monitoring method, medium and system | |
| Penmetcha et al. | Computer vision-based algae removal planner for multi-robot teams | |
| CN112885021B (en) | Multi-sensor fire prediction method and system based on composite algorithm | |
| CN113834523B (en) | Marine pasture intelligent breeding system based on unmanned ship | |
| CN102496062A (en) | Personnel information fusion method based on Spiking neural network | |
| CN109902647A (en) | The portable online Bird's Nest intelligent identification Method of one kind and system | |
| Gai et al. | Research on water quality spatiotemporal forecasting model based on ST-BIGRU-SVR neural network | |
| CN114445924A (en) | System and method for safely inspecting operating personnel of culture ship | |
| CN114723131A (en) | Mine environment intelligent prediction system based on artificial intelligence | |
| Panahi et al. | Cooperative Unmanned Aerial Vehicles for Monitoring and Maintenance of Heat and Electricity Incorporated Networks: A Learning‐based Approach | |
| CN209541789U (en) | A kind of underwater robot detection system for the detection of bridge submerged structure |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |