CN118192603A - A cruising unmanned vehicle for fish ponds and a method of using the same - Google Patents
A cruising unmanned vehicle for fish ponds and a method of using the same Download PDFInfo
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/40—Control within particular dimensions
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- G05D1/242—Means based on the reflection of waves generated by the vehicle
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- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/20—Control system inputs
- G05D1/24—Arrangements for determining position or orientation
- G05D1/243—Means capturing signals occurring naturally from the environment, e.g. ambient optical, acoustic, gravitational or magnetic signals
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/20—Control system inputs
- G05D1/24—Arrangements for determining position or orientation
- G05D1/246—Arrangements for determining position or orientation using environment maps, e.g. simultaneous localisation and mapping [SLAM]
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- G05D1/20—Control system inputs
- G05D1/24—Arrangements for determining position or orientation
- G05D1/247—Arrangements for determining position or orientation using signals provided by artificial sources external to the vehicle, e.g. navigation beacons
- G05D1/248—Arrangements for determining position or orientation using signals provided by artificial sources external to the vehicle, e.g. navigation beacons generated by satellites, e.g. GPS
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/60—Intended control result
- G05D1/617—Safety or protection, e.g. defining protection zones around obstacles or avoiding hazards
- G05D1/622—Obstacle avoidance
- G05D1/633—Dynamic obstacles
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/60—Intended control result
- G05D1/644—Optimisation of travel parameters, e.g. of energy consumption, journey time or distance
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Abstract
本发明公开了一种鱼塘用巡航无人车及其使用方法，包括无人车主体，无人车主体外部设置有移动控制机构、电源模块、智能化决策支持模块、异常检测与预警模块、自动巡航与路径规划模块、通信模块与管理装置；环境监测与数据收集模块：用于实时收集鱼塘的各项环境参数；自动巡航与路径规划模块：用于自主规划出最优巡航路径；异常检测与预警模块：用于识别鱼塘中的异常情况并进行预警；本发明中，各个模块搭载在无人车设备，通过自动巡航与路径规划模块实现自动化巡检，利用环境监测与数据收集模块进行采样，定时巡视鱼塘各个区域，采集水质样本等数据，减少人力投入，提高效率。
The invention discloses a cruising unmanned vehicle for a fish pond and a method for using the same, comprising an unmanned vehicle main body, wherein a mobile control mechanism, a power module, an intelligent decision support module, an abnormality detection and early warning module, an automatic cruising and path planning module, a communication module and a management device are arranged on the outside of the unmanned vehicle main body; an environmental monitoring and data collection module: used for real-time collection of various environmental parameters of the fish pond; an automatic cruising and path planning module: used for autonomously planning an optimal cruising path; an abnormality detection and early warning module: used for identifying abnormal situations in the fish pond and issuing early warnings; in the invention, each module is mounted on the unmanned vehicle equipment, automatic inspection is realized through the automatic cruising and path planning module, sampling is performed using the environmental monitoring and data collection module, various areas of the fish pond are patrolled regularly, data such as water quality samples are collected, manpower input is reduced, and efficiency is improved.
Description
Technical Field
The invention relates to the technical field of fishpond management, in particular to a cruising unmanned vehicle for a fishpond and a using method thereof.
Background
With the growing population and increasing concern for food safety, aquaculture has become one of the important areas to meet people's needs. In aquaculture, sang Ji fishponds are used as a traditional aquaculture mode, have the advantages of low cost, good water quality, environmental friendliness and the like, and are widely applied. Meanwhile, unmanned vehicle technology is becoming mature and shows great application potential in various fields.
Through retrieval, the invention patent with the Chinese patent number of CN113687614A discloses a fish pond monitoring system based on a cloud database, which comprises a temperature sensor, a PH sensor, a dissolved oxygen sensor, an ultrasonic ranging module, a cloud platform module, a WeChat applet and a main control singlechip which are arranged in fish pond water; the singlechip forms a serial port communication module through an ESP8266WI F I module and communicates with the cloud platform module to transmit data; the cloud platform module obtains the query requirement of the WeChat applet through the f/ask network application framework, queries the data stored in the i nf l uxDB database according to the requirement, counts the maximum value, the minimum value, the time reaching the maximum value and the minimum value and the causal trend of the data, returns the result to the WeChat applet finally, receives the data statistics result, and displays the data in the form of a line graph by utilizing the canvas function.
Compared with the prior art, the invention patent with the Chinese patent number of CN113687614A can realize real-time monitoring of important data in the fishpond under an unmanned environment, and timely feeds back the important data to a user, thereby providing reliable data for the user to monitor and manage the fishpond in real time.
However, in the actual use process of the device, the manager can only judge through the past experience, and the management of the base fish pond is manually interfered, so that the manager needs to invest a great deal of time and energy, and management errors or negligence are easy to occur.
Through retrieval, the invention patent with the Chinese patent number of CN106681217A discloses a multifunctional real-time management system for a fish pond, which solves the technical problems that the conventional method not only consumes a great deal of energy and time of farmers, but also increases a plurality of electric power running costs; the communication module can carry out wireless data transmission of mobile phones with farmers; the sensing module, the control module, the execution module and the communication module are respectively connected with the natural energy power supply module.
Compared with the prior art, the Chinese patent No. CN113834523B adopts full natural energy-solar energy as the energy source required by the fish pond real-time management system, so that the carbon emission can be reduced, the environment is protected, the installation of cables can be avoided, the electric energy is saved, and the cultivation cost is reduced; the fishpond environment is detected by adopting various sensors, and the wireless data transmission function of the mobile phone based on GSM is realized, so that the real-time monitoring and control of the fishpond condition by farmers can be realized.
However, in the actual use process of the device, the Sang Ji fishponds are generally distributed in different regions, so that the conditions of all the ponds are monitored, the coverage range of monitoring equipment is limited, and real-time monitoring of all the regions cannot be realized, and therefore, the cruise unmanned vehicle for the fishponds and the use method thereof are provided.
Disclosure of Invention
The invention aims to solve the defects that in the prior art, a manager can only judge through the past experience and manually interfere with the management of a base fish pond, so that a great amount of time and energy are needed to be input by the manager, management errors or negligence are easy to occur, and in addition, the Sang Ji fish ponds are generally distributed in different regions, the condition of monitoring each pond opening is monitored, the coverage area of monitoring equipment is limited, and real-time monitoring of all areas cannot be realized.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
The cruising unmanned vehicle for the fishpond comprises an unmanned vehicle main body, wherein a mobile control mechanism, a power supply module, an intelligent decision support module, an abnormality detection and early warning module, an automatic cruising and path planning module, a communication module and a management device are arranged outside the unmanned vehicle main body;
the environment monitoring and data collecting module: all environmental parameters of the fishpond are collected in real time;
Automatic cruising and path planning module: the method is used for autonomously planning an optimal cruising path;
abnormality detection and early warning module: the method comprises the steps of identifying abnormal conditions in a fish pond and carrying out early warning;
An intelligent decision support module: the method comprises the steps of integrating and analyzing historical data and real-time data, constructing a prediction model, and assisting management personnel in formulating a cultivation strategy;
Management device: the device is used for accurate feeding and emergency treatment;
And a communication module: the system is used for realizing real-time data transmission between the unmanned vehicle and the central management system;
The environment monitoring and data collecting module transmits collected data to the intelligent decision support module and the abnormality detection and early warning module, the automatic cruising and path planning module receives a cruising instruction of the central management system and plans an optimal cruising path by combining the data monitored by the environment monitoring and data collecting module, once abnormality is found, the abnormality detection and early warning module triggers the early warning system and sends abnormality information to the intelligent decision support module, the intelligent decision support module receives the monitoring data of the environment monitoring and data collecting module and the abnormality information sent by the intelligent decision support module to analyze and predict the monitoring data and the abnormality information, the intelligent decision support module sends an instruction to the management device based on an analysis result, a control system is arranged outside the mobile control mechanism, and the power module is externally connected with a solar panel to provide clean energy.
The technical scheme further comprises the following steps:
The environment monitoring and data collecting module comprises a sensor array unit, an image collecting unit and a data processing unit, wherein the sensor array unit is used for collecting various environmental parameters of the fish pond in real time, the sensor array unit comprises a laser radar, the image collecting unit is used for shooting images and videos in the fish pond and capturing various visual information of the dynamic state and the water surface state of the fish shoal, the image collecting unit comprises a RPLIDARA radar, and the data processing unit is used for processing the data collected by the sensor array unit and the image collecting unit;
The RPLIDARA radar adopts a laser triangle ranging technology, is matched with an independently developed high-speed vision acquisition processing mechanism, can perform ranging actions more than once per second, and the ranging core of the RPLIDARA radar rotates clockwise, so that the omnidirectional scanning ranging detection of the surrounding environment can be realized, and the profile diagram of the surrounding environment is obtained.
The laser radar scans and processes data of an environment where the unmanned vehicle is located, a grid map is constructed by using laser radar data, for a point, p (s=1) is used for representing the probability that the point is in an occupied state, p (s=0) is used for representing the probability that the point is in an idle state, and the sum of the two must be equal to 1, namely:
p(s＝1)+p(s＝0)＝1；
to reduce the repetition rate, the state of the point is expressed using the ratio of the two, namely:
the ratio of the occupancy probability to the idle probability obtains a value reflecting the state of the point, and the value reflecting the state of the point accurately judges whether the point is in the occupancy state or the idle state;
for a point, assuming that the state of the point is Odd(s) before a new measured value is obtained, updating the point after obtaining the new measured value The expression p (s|z) represents the state under the condition of occurrence, and is obtained according to the Bayesian formulaAnd (3) withBring it intoThe two sides take logarithm to obtainWhereinIs a variable, labeled lomcas, when the measured value z is 0, labeled lofre, and conversely, labeled looccu, both of which are constant values;
Let the state of the point before measurement to logOdd (S) be S n-1, the state of the point after measurement to logOdd (s|z) be S n, and result in S n＝Sn-1 + lomcas, and if the point has no initial state of measurement, the initial state of the point is 0.
The automatic cruising and path planning module comprises a Beidou satellite system, a map data collecting unit and a path planning unit, wherein the Beidou satellite system is used for acquiring current geographic position coordinates of the unmanned vehicle, the map data collecting unit is used for collecting various map data of fish pond topographic information and obstacle positions, the path planning unit is used for calculating an optimal cruising path according to the map data and the current position by using a path planning algorithm, the automatic cruising and path planning module acquires real-time position information of the unmanned vehicle through the Beidou satellite system and transmits the real-time position information to the path planning unit, and the path planning unit performs path planning by using the position information and the map data and sends the calculated optimal path to a control system of the unmanned vehicle to guide the unmanned vehicle to perform corresponding cruising actions.
The management device comprises a breeding feeding module and an emergency processing module, the breeding feeding module receives feeding instructions from the intelligent decision support module, calculates and controls the feeding device to accurately feed, meanwhile, the environment monitoring and data collecting module collects data in the feeding process and feeds the data back to the intelligent decision support module to evaluate effects, and the emergency processing module immediately starts an emergency processing flow after receiving early warning information of the abnormality detection and early warning module.
The abnormal detection and early warning module comprises a data collection and preprocessing unit, a characteristic extraction unit, an abnormal detection unit, an early warning control unit and an alarm unit, wherein the data collection and preprocessing unit is used for collecting various data from a fish pond and preprocessing the collected data, the characteristic extraction unit is used for extracting characteristics required by the abnormal detection unit, the abnormal detection unit is used for real-time analysis, abnormal conditions in the fish pond are identified by comparing historical data, establishing a model or setting a threshold value, the early warning control unit is used for determining early warning triggering conditions and notification modes, the alarm unit comprises an audible-visual alarm, a short message notification system and the like, and the alarm unit is used for sending early warning information to management personnel.
The intelligent decision support module comprises a data processing unit, a big data analysis unit, a machine learning model construction unit and a decision support unit, wherein the data processing unit is used for collecting historical data and real-time data and processing the collected data, the big data analysis unit is used for carrying out deep analysis on the processed data by utilizing statistical analysis and identifying modes, trends and relativity in the data, the machine learning model construction unit is used for constructing a model capable of predicting future trends and results, the decision support unit is used for providing specific decision suggestions according to the prediction results of the machine learning model in combination with experience and business requirements of management staff, assisting the management staff in making cultivation strategies, the data collection and preprocessing unit is used for transmitting the processed data to the big data analysis unit for carrying out deep analysis and excavation on the big data analysis unit, the analysis results of the big data analysis unit are transmitted to the management staff through a visual tool on one hand, the machine learning model construction unit is used as input data for training and optimizing of a model on the other hand, the model trained by the machine learning model construction unit can periodically or in real-time transmit the prediction results to the decision support unit, the prediction results are transmitted to the decision support unit, the decision support unit is used for forming decision support unit, and the decision support unit is used for carrying out decision support unit, and the decision support unit is used for forming the decision support unit, and the optimal data are transmitted to the decision support unit, and the decision support unit is used for carrying out the interactive and the decision process.
The specific steps of the machine learning model building unit building a model are as follows:
Data preparation: selecting a proper data set from the historical data collected by the big data analysis unit, wherein the data should contain various parameters and characteristics related to the fish pond environment, the fish shoal growth and the like, and preprocessing and cleaning the data, including processing missing values, abnormal values, standardized data and the like;
Model selection: selecting an appropriate countermeasure network structure to construct a machine learning model, wherein the countermeasure network consists of a generator and a arbiter which compete with each other to improve the performance of the generator, and determining the architecture of the generator and the arbiter, including the number of network layers, the number of nodes of each layer, an activation function and the like;
Model training: inputting the prepared data set into a generator and a discriminator, starting a training process of a model, wherein the generator tries to generate synthetic data similar to real data, the discriminator tries to distinguish the real data from the synthetic data, and the generator and the discriminator are continuously optimized by alternately training the generator and the discriminator until the generator generates high-quality synthetic data, and the discriminator cannot easily distinguish the real data from the synthetic data;
model evaluation: evaluating the performance of the model using evaluation metrics such as a loss function of the generator, an accuracy of the arbiter, etc., cross-validation or retention datasets may be used to verify the generalization ability of the model, and to adjust the hyper-parameters of the model to improve performance;
model application: the current environmental parameters and conditions, such as the growth condition of fish shoal, water quality condition and the like, are input, and the generator is used for generating synthetic data to predict the future fish pond condition, and the prediction results can be used as the basis of a decision support system to help management staff to formulate reasonable cultivation strategies and countermeasures.
The application method of the cruising unmanned vehicle for the fishpond comprises the following steps:
Starting and initializing: the unmanned vehicle main body performs self-checking firstly, ensures that all sensors, control systems and executing mechanisms are in a normal working state, and loads a preset cruising route and task;
Auto-cruise and data collection: according to a preset cruising route, the unmanned vehicle main body starts to run in the fishpond autonomously, the environment monitoring and data collecting module collects environment parameters of the fishpond in real time, captures visual information of the fishpond, and transmits data to the central management system through the communication module;
Data analysis and anomaly detection: the central management system receives data from the unmanned vehicle main body, and performs real-time analysis by utilizing the intelligent decision support module, and the abnormality detection and early warning module identifies abnormal conditions in the fishpond;
Intelligent decision support: based on the data analysis result, the intelligent decision support module provides cultivation strategy advice for management personnel, and the unmanned vehicle main body automatically calculates cultivation feeding amount and feeds the cultivation feeding amount through a management device;
Emergency treatment: once an abnormal situation is found, the unmanned vehicle main body immediately starts an emergency treatment process, and according to a preset emergency treatment strategy, the unmanned vehicle main body automatically adjusts the environmental parameters of the fishpond through the management device, and meanwhile, the unmanned vehicle main body sends an early warning to a manager through an abnormality detection and early warning module, so that timely taking of manual intervention measures is ensured;
And (3) data feedback and adjustment: the unmanned vehicle main body feeds back the processing result and the new data to the central management system, a manager further optimizes the cultivation strategy according to the information, and the manager can adjust parameters such as cruising route, task and data collection frequency of the unmanned vehicle according to the actual condition and cultivation requirement of the fishpond;
And (3) circularly running: after the unmanned vehicle body finishes one-time cruising, the unmanned vehicle body can enter the next round of circulating operation according to the instruction of the central management system, and the condition of the fish pond is continuously monitored.
The invention has the following beneficial effects:
1. according to the invention, each module is mounted on unmanned vehicle equipment, automatic inspection is realized through the automatic cruising and path planning module, the environment monitoring and data collecting module is utilized for sampling, each area of the fish pond is inspected at regular time, and data such as water quality samples are collected, so that the manpower input is reduced, and the efficiency is improved.
2. In the invention, the intelligent decision support module combines the artificial intelligence technology, the abnormality detection and early warning module and the intelligent decision support module combine to automatically identify abnormal conditions and send out early warning prompts, thereby helping management staff to process problems in time and reducing risk intellectualization.
Drawings
Fig. 1 is a schematic structural view of a cruising unmanned vehicle for a fish pond and a using method thereof;
Fig. 2 is a flow chart of the present invention.
In the figure: 1. an unmanned vehicle body; 2. a movement control mechanism; 3. a power module; 4. an intelligent decision support module; 5. an abnormality detection and early warning module; 6. an automatic cruising and path planning module; 7. a communication module; 8. a management device; 9. and the environment monitoring and data collecting module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1 and 2, the present invention is a cruising unmanned vehicle for a fish pond, comprising an unmanned vehicle body 1, wherein a mobile control mechanism 2, a power module 3, an intelligent decision support module 4, an anomaly detection and early warning module 5, an automatic cruising and path planning module 6, a communication module 7 and a management device 8 are arranged outside the unmanned vehicle body 1;
The environmental monitoring and data collection module 9: all environmental parameters of the fishpond are collected in real time;
Automatic cruise and path planning module 6: the method is used for autonomously planning an optimal cruising path;
abnormality detection and early warning module 5: the method comprises the steps of identifying abnormal conditions in a fish pond and carrying out early warning;
intelligent decision support module 4: the method comprises the steps of integrating and analyzing historical data and real-time data, constructing a prediction model, and assisting management personnel in formulating a cultivation strategy;
management device 8: the device is used for accurate feeding and emergency treatment;
communication module 7: the system is used for realizing real-time data transmission between the unmanned vehicle and the central management system;
The environment monitoring and data collecting module 9 transmits the collected data to the intelligent decision support module 4 and the abnormality detecting and early warning module 5, the automatic cruising and path planning module 6 receives a cruising instruction of the central management system and plans an optimal cruising path by combining the data monitored by the environment monitoring and data collecting module 9, once abnormality is found, the abnormality detecting and early warning module 5 triggers the early warning system and sends abnormal information to the intelligent decision support module 4, the intelligent decision support module 4 receives the monitoring data of the environment monitoring and data collecting module 9 and the abnormal information sent by the intelligent decision support module 4 to analyze and predict the abnormal information, the intelligent decision support module 4 sends an instruction to the management device 8 based on an analysis result, a control system is arranged outside the mobile control mechanism 2, and the power module 3 is externally connected with a solar panel to provide clean energy.
The invention provides a cruising unmanned vehicle for a fish pond and a use method thereof, and the working principle of the cruising unmanned vehicle is that an environment monitoring and data collecting module 9 sensor array unit and an image collecting unit on a unmanned vehicle main body 1 continuously collect data of the fish pond environment, including water temperature, water quality, fish activity condition and the like, and the data are converted into information which can be understood and processed by the unmanned vehicle main body 1 after preliminary processing, and an intelligent decision support module 4, an abnormality detection and early warning module 5 and an automatic cruising and path planning module 6 receive the data from the environment monitoring and data collecting module 9 and carry out deep analysis on the data;
According to a preset algorithm and rule, the unmanned vehicle main body 1 can evaluate the fishpond environment and determine the next action, such as adjusting a cruising route, throwing feed, and the like, once the intelligent decision support module 4 and the automatic cruising and path planning module 6 make a decision, the intelligent decision support module can send a command to the mobile control mechanism 2 and the management device 8, the mobile control mechanism 2 and the management device 8 can immediately start to execute the corresponding action after receiving the command, the mobile control mechanism 2 can adjust the running path of the unmanned vehicle main body 1 according to the command, and the management device 8 can throw a proper amount of feed according to the command;
The mobile control mechanism 2 and the management device 8 continuously send feedback data to the intelligent decision support module 4, the abnormality detection and early warning module 5 and the automatic cruising and route planning module 6 when executing actions, including execution states, encountered problems and the like, and the intelligent decision support module 4, the abnormality detection and early warning module 5 and the automatic cruising and route planning module 6 can monitor and adjust the operation of the unmanned vehicle main body 1 in real time according to the feedback data, so that the task can be successfully completed, and all data generated by the unmanned vehicle main body 1 in the operation process, including collected environmental data, system decisions, execution instructions, feedback information and the like, can be recorded and stored in corresponding storage equipment.
In one embodiment, for the above-mentioned environmental monitoring and data collecting module 9, the environmental monitoring and data collecting module 9 includes a sensor array unit, an image collecting unit and a data processing unit, where the sensor array unit is used for collecting various environmental parameters of the fish pond in real time, the sensor array unit includes a laser radar, the image collecting unit is used for shooting images and videos in the fish pond, capturing various visual information of fish shoal dynamic and water surface conditions, the image collecting unit includes RPLIDARA radar, and the data processing unit is used for processing the data collected by the sensor array unit and the image collecting unit;
The RPLIDARA radar adopts a laser triangle ranging technology, is matched with an independently developed high-speed vision acquisition processing mechanism, can perform more than 8000 ranging actions per second, and the ranging core of the RPLIDARA radar rotates clockwise, so that 360-degree omnibearing scanning ranging detection on the surrounding environment can be realized, and a contour map of the surrounding environment is obtained.
In one embodiment, for the above-mentioned lidar, the lidar scans and processes data of the environment where the unmanned vehicle is located, and constructs a grid map using the lidar data, and for a point, p (s=1) represents the probability that the point is in an occupied state, and p (s=0) represents the probability that the point is in an idle state, where the sum of the two must be equal to 1: p (s=1) +p (s=0) =1;
to reduce the repetition rate, the state of the point is expressed using the ratio of the two, namely:
the ratio of the occupancy probability to the idle probability obtains a value reflecting the state of the point, and the value reflecting the state of the point accurately judges whether the point is in the occupancy state or the idle state;
for a point, assuming that the state of the point is Odd(s) before a new measured value is obtained, updating the point after obtaining the new measured value The expression p (s|z) represents the state under the condition of occurrence, and is obtained according to the Bayesian formulaAnd (3) withBring it intoThe two sides take logarithm to obtainWhereinIs a variable, labeled lomcas, when the measured value z is 0, labeled lofre, and conversely, labeled looccu, both of which are constant values;
Let the state of the point before measurement to logOdd (S) be S n-1, the state of the point after measurement to logOdd (s|z) be S n, and result in S n＝Sn-1 + lomcas, and if the point has no initial state of measurement, the initial state of the point is 0.
In one embodiment, for the above-mentioned automatic cruising and path planning module 6, the automatic cruising and path planning module 6 includes a beidou satellite system, a map data collecting unit and a path planning unit, the beidou satellite system is used for obtaining current geographic position coordinates of the unmanned vehicle, the map data collecting unit is used for collecting various map data of fish pond topographic information and obstacle positions, the path planning unit is used for calculating an optimal cruising path according to the map data and the current position by using a path planning algorithm, the automatic cruising and path planning module 6 obtains real-time position information of the unmanned vehicle by using the beidou satellite system and transmits the real-time position information to the path planning unit, and the path planning unit performs path planning by using the position information and the map data and transmits the calculated optimal path to a control system of the unmanned vehicle to guide the unmanned vehicle to perform corresponding cruising action.
In one embodiment, for the management device 8, the management device 8 includes a feeding module for cultivation and an emergency processing module, the feeding module for cultivation receives a feeding instruction from the intelligent decision support module 4, calculates and controls the feeding device to accurately feed, meanwhile, the environmental monitoring and data collecting module 9 collects data in the feeding process, feeds the data back to the intelligent decision support module 4 to evaluate the effect, and the emergency processing module immediately starts an emergency processing flow after receiving the early warning information of the abnormality detection and early warning module 5.
In one embodiment, for the abnormality detection and early warning module 5, the abnormality detection and early warning module 5 includes a data collection and preprocessing unit, a feature extraction unit, an abnormality detection unit, an early warning control unit, and an alarm unit, where the data collection and preprocessing unit is used to collect various data from the fish pond and preprocess the collected data, the feature extraction unit is used to extract features required by the abnormality detection unit, the abnormality detection unit is used to analyze in real time, and identify abnormal conditions in the fish pond by comparing historical data, modeling or setting a threshold value, the early warning control unit is used to determine early warning trigger conditions and notification modes, the alarm unit includes an audible-visual alarm, a short message notification system, and the alarm unit is used to send early warning information to a manager.
In one embodiment, for the intelligent decision support module 4, the intelligent decision support module 4 includes a data processing unit, a big data analysis unit, a machine learning model construction unit and a decision support unit, where the data processing unit is configured to collect historical data and real-time data, process the collected data, the big data analysis unit is configured to utilize statistical analysis to perform deep analysis on the processed data, identify patterns, trends and correlations in the data, the machine learning model construction unit is configured to construct a model capable of predicting future trends and results, the decision support unit is configured to provide specific decision suggestions according to the prediction results of the machine learning model, combine experience and business requirements of a manager, assist the manager to make cultivation strategies, the data collection and preprocessing unit is configured to transmit the processed data to the big data analysis unit for deep analysis and mining, the analysis results of the big data analysis unit are presented to the manager through a visual tool, and serve as input data to the machine learning model construction unit for training and optimization of the model, the model trained by the machine learning model construction unit is configured to transmit the prediction results to the decision support unit or to the decision support unit in real-time, and periodically form the interactive and optimizing the data according to the prediction results, and the requirements are formed by the decision support unit, and the interactive and optimizing the data are formed.
In one embodiment, for the machine learning model building unit, the specific steps of the machine learning model building unit building a model are:
Data preparation: selecting a proper data set from the historical data collected by the big data analysis unit, wherein the data should contain various parameters and characteristics related to the fish pond environment, the fish shoal growth and the like, and preprocessing and cleaning the data, including processing missing values, abnormal values, standardized data and the like;
Model selection: selecting a proper countermeasure network structure to construct a machine learning model, wherein the countermeasure network consists of a generator and a discriminator which compete with each other to improve the performance of the generator, and determining the architecture of the generator and the discriminator, including the number of network layers, the number of nodes of each layer, an activation function and the like;
model training: inputting the prepared data set into a generator and a discriminator, starting the training process of the model, wherein the generator tries to generate synthetic data similar to the real data, the discriminator tries to distinguish the real data from the synthetic data, and the generator and the discriminator are trained alternately to continuously optimize the real data and the synthetic data until the generator generates high-quality synthetic data, and the discriminator cannot easily distinguish the real data and the synthetic data;
model evaluation: evaluating the performance of the model using evaluation metrics such as a loss function of the generator, an accuracy of the arbiter, etc., cross-validation or retention datasets may be used to verify the generalization ability of the model, and to adjust the hyper-parameters of the model to improve performance;
model application: the current environmental parameters and conditions, such as the growth condition of fish shoal, water quality condition and the like, are input, and the generator is used for generating synthetic data to predict the future fish pond condition, and the prediction results can be used as the basis of a decision support system to help management staff to formulate reasonable cultivation strategies and countermeasures.
The application method of the cruising unmanned vehicle for the fishpond comprises the following steps:
starting and initializing: the unmanned vehicle main body 1 performs self-checking firstly, ensures that all sensors, control systems and executing mechanisms are in a normal working state, and loads a preset cruising route and task;
auto-cruise and data collection: according to a preset cruising route, the unmanned vehicle main body 1 starts to run in the fishpond autonomously, the environment monitoring and data collecting module 9 collects environment parameters of the fishpond in real time, captures visual information of the fishpond, and transmits data to the central management system through the communication module 7;
Data analysis and anomaly detection: the central management system receives data from the unmanned vehicle main body 1, and performs real-time analysis by utilizing the intelligent decision support module 4, and the abnormality detection and early warning module 5 identifies abnormal conditions in the fishpond;
intelligent decision support: based on the data analysis result, the intelligent decision support module 4 provides cultivation strategy advice for the management personnel, and the unmanned vehicle main body 1 automatically calculates cultivation feeding amount and feeds through the management device 8;
Emergency treatment: once an abnormal situation is found, the unmanned vehicle main body 1 immediately starts an emergency treatment process, the unmanned vehicle main body 1 automatically adjusts the environmental parameters of the fishpond through the management device 8 according to a preset emergency treatment strategy, and meanwhile, the unmanned vehicle main body 1 sends out early warning to management personnel through the abnormality detection and early warning module 5, so that timely manual intervention measures are ensured to be adopted;
And (3) data feedback and adjustment: the unmanned vehicle main body 1 feeds back the processing result and the new data to the central management system, a manager further optimizes the cultivation strategy according to the information, and the manager can adjust parameters such as the cruising route, the task, the data collection frequency and the like of the unmanned vehicle according to the actual condition and the cultivation requirement of the fishpond;
And (3) circularly running: after the unmanned vehicle body 1 finishes one-time cruising, the unmanned vehicle body can enter the next round of circulating operation according to the instruction of the central management system, and the condition of the fish pond is continuously monitored.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. The cruising unmanned vehicle for the fishpond is characterized by comprising an unmanned vehicle main body (1), wherein a mobile control mechanism (2), a power supply module (3), an intelligent decision support module (4), an abnormality detection and early warning module (5), an automatic cruising and path planning module (6), a communication module (7) and a management device (8) are arranged outside the unmanned vehicle main body (1);
an environment monitoring and data collection module (9): all environmental parameters of the fishpond are collected in real time;
An auto-cruise and path planning module (6): the method is used for autonomously planning an optimal cruising path;
abnormality detection and early warning module (5): the method comprises the steps of identifying abnormal conditions in a fish pond and carrying out early warning;
intelligent decision support module (4): the method comprises the steps of integrating and analyzing historical data and real-time data, constructing a prediction model, and assisting management personnel in formulating a cultivation strategy;
Management device (8): the device is used for accurate feeding and emergency treatment;
communication module (7): the system is used for realizing real-time data transmission between the unmanned vehicle and the central management system;
The environment monitoring and data collection module (9) transmits collected data to the intelligent decision support module (4) and the abnormality detection and early warning module (5), the automatic cruising and path planning module (6) receives a cruising instruction of the central management system, an optimal cruising path is planned by combining the data monitored by the environment monitoring and data collection module (9), once abnormality is found, the abnormality detection and early warning module (5) triggers the early warning system and sends abnormality information to the intelligent decision support module (4), the intelligent decision support module (4) receives monitoring data of the environment monitoring and data collection module (9) and the abnormality information sent by the intelligent decision support module (4) to analyze and predict the abnormality information, and based on an analysis result, the intelligent decision support module (4) sends an instruction to the management device (8), and the mobile control mechanism (2) is externally provided with a control system.
2. The cruising unmanned vehicle for the fishpond according to claim 1, wherein the environment monitoring and data collecting module (9) comprises a sensor array unit, an image collecting unit and a data processing unit, the sensor array unit is used for collecting various environment parameters of the fishpond in real time, the sensor array unit comprises a laser radar, the image collecting unit is used for shooting images and videos in the fishpond and capturing various visual information of the dynamic and water surface conditions of the fishpond, the image collecting unit comprises a RPLIDARA radar, and the data processing unit is used for processing data collected by the sensor array unit and the image collecting unit.
3. The cruising unmanned vehicle for a fish pond according to claim 2, wherein the laser radar scans and processes data of an environment where the unmanned vehicle is located, a grid map is constructed by using laser radar data, a probability that a point is in an occupied state is represented by p (s=1), a probability that the point is in an idle state is represented by p (s=0), and a sum of the two must be equal to 1: p (s=1) +p (s=0) =1;
to reduce the repetition rate, the state of the point is expressed using the ratio of the two, namely:
the ratio of the occupancy probability to the idle probability obtains a value reflecting the state of the point, and the value reflecting the state of the point accurately judges whether the point is in the occupancy state or the idle state;
for a point, assuming that the state of the point is Odd(s) before a new measured value is obtained, updating the point after obtaining the new measured value The expression p (s|z) represents the state under the condition of occurrence, and is obtained according to the Bayesian formulaAnd (3) withBring it intoThe two sides take logarithm to obtainWhereinIs a variable, labeled lomcas, when the measured value z is 0, labeled lofre, and conversely, labeled looccu, both of which are constant values;
Let the state of the point before measurement to logOdd (S) be S n-1, the state of the point after measurement to logOdd (s|z) be S n, and result in S n＝Sn-1 + lomcas, and if the point has no initial state of measurement, the initial state of the point is 0.
4. The unmanned fish pond cruising vehicle according to claim 1, wherein the automatic cruising and path planning module (6) comprises a Beidou satellite system, a map data collecting unit and a path planning unit, the Beidou satellite system is used for acquiring current geographic position coordinates of the unmanned fish pond, the map data collecting unit is used for collecting various map data of fish pond topographic information and obstacle positions, the path planning unit is used for calculating an optimal cruising path according to the map data and the current position by using a path planning algorithm, the automatic cruising and path planning module (6) acquires real-time position information of the unmanned fish pond vehicle through the Beidou satellite system and transmits the real-time position information to the path planning unit, and the path planning unit performs path planning by utilizing the position information and the map data and transmits the calculated optimal path to a control system of the unmanned fish pond, so as to guide the unmanned fish pond cruising action.
5. The cruising unmanned vehicle for the fishpond according to claim 1, wherein the management device (8) comprises a breeding feeding module and an emergency processing module, the breeding feeding module receives feeding instructions from the intelligent decision support module (4), calculates and controls the feeding device to accurately feed, meanwhile, the environment monitoring and data collecting module (9) collects data in the feeding process and feeds the data back to the intelligent decision support module (4) to evaluate effects, and the emergency processing module immediately starts an emergency processing flow after receiving early warning information of the abnormality detection and early warning module (5).
6. The cruising unmanned vehicle for a fish pond according to claim 1, wherein the abnormality detection and early warning module (5) comprises a data collection and preprocessing unit, a feature extraction unit, an abnormality detection unit, an early warning control unit and an alarm unit, wherein the data collection and preprocessing unit is used for collecting various data from the fish pond and preprocessing the collected data, the feature extraction unit is used for extracting features required by the abnormality detection unit, the abnormality detection unit is used for analyzing in real time, identifying abnormal conditions in the fish pond by comparing historical data, establishing a model or setting a threshold value, the early warning control unit is used for determining early warning triggering conditions and notification modes, and the alarm unit is used for sending early warning information to a manager.
7. The method for using the cruising unmanned vehicle for the fishpond according to claim 1, wherein the intelligent decision support module (4) comprises a data processing unit, a big data analysis unit, a machine learning model construction unit and a decision support unit, wherein the data processing unit is used for collecting historical data and real-time data, processing the collected data, the big data analysis unit is used for carrying out deep analysis on the processed data by using statistical analysis, identifying modes, trends and relativity in the data, the machine learning model construction unit is used for constructing a model capable of predicting future trends and results, the decision support unit is used for combining the experience and business requirements of a manager according to the prediction results of the machine learning model, providing concrete decision suggestions to assist the manager in making a cultivation strategy, the data collecting and preprocessing unit is used for transmitting the processed data to the big data analysis unit for deep analysis and mining, the analysis result of the big data analysis unit is displayed to the manager through a visual tool, the machine learning model construction unit is used as input data for carrying out deep analysis on the other hand, the model capable of predicting future trends and results is used for forming a decision-making model by the decision-making unit, the decision-making unit is used for forming a decision-making model by the optimal decision-making unit or the decision-making unit is used for forming the decision-making model and the optimal decision-making unit and the decision-making model.
8. The cruising unmanned vehicle for a fish pond and the using method thereof according to claim 7, wherein the specific steps of the machine learning model building unit building a model are as follows:
data preparation: selecting a proper data set from the historical data collected by the big data analysis unit, and preprocessing and cleaning the data;
Model selection: selecting an appropriate countermeasure network structure to construct a machine learning model, wherein the countermeasure network consists of a generator and a discriminator, and the architecture of the generator and the discriminator is determined;
Model training: inputting the prepared data set into a generator and a discriminator, starting a training process of a model, wherein the generator tries to generate synthetic data similar to real data, the discriminator tries to distinguish the real data from the synthetic data, and the generator and the discriminator are continuously optimized by alternately training the generator and the discriminator until the generator generates high-quality synthetic data, and the discriminator cannot easily distinguish the real data from the synthetic data;
model evaluation: evaluating performance of the model using the evaluation index;
Model application: current environmental parameters and conditions are entered and synthetic data is generated using a generator to predict future pond conditions.
9. The method of using a cruising unmanned vehicle for a fish pond according to claim 1, comprising the steps of:
starting and initializing: the unmanned vehicle main body (1) performs self-checking firstly, ensures that all sensors, control systems and executing mechanisms are in a normal working state, and loads a preset cruising route and tasks;
auto-cruise and data collection: according to a preset cruising route, the unmanned vehicle main body (1) starts to run in the fishpond autonomously, the environment monitoring and data collecting module (9) collects environment parameters of the fishpond in real time, captures visual information of the fishpond, and transmits data to a central management system through the communication module (7);
Data analysis and anomaly detection: the central management system receives data from the unmanned vehicle main body (1) and performs real-time analysis by utilizing the intelligent decision support module (4), and the abnormality detection and early warning module (5) identifies abnormal conditions in the fishpond;
Intelligent decision support: based on the data analysis result, the intelligent decision support module (4) provides cultivation strategy suggestions for management personnel, and the unmanned vehicle main body (1) automatically calculates cultivation feeding amount and feeds the cultivation feeding amount through the management device (8);
Emergency treatment: once an abnormal situation is found, the unmanned vehicle main body (1) immediately starts an emergency treatment process, and according to a preset emergency treatment strategy, the unmanned vehicle main body (1) automatically adjusts the environmental parameters of the fishpond through the management device (8), and meanwhile, the unmanned vehicle main body (1) sends an early warning to a manager through the abnormality detection and early warning module (5) so as to ensure that manual intervention measures are timely adopted;
And (3) data feedback and adjustment: the unmanned vehicle main body (1) feeds back the processing result and the new data to a central management system, and management personnel further optimize the cultivation strategy according to the information;
and (3) circularly running: after the unmanned vehicle main body (1) finishes one-time cruising, the unmanned vehicle main body can enter the next round of circulating operation according to the instruction of the central management system, and the condition of the fish pond is continuously monitored.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118664622A (en) * | 2024-08-21 | 2024-09-20 | 中交机电工程局有限公司 | Unmanned track inspection robot |
| CN119814976A (en) * | 2024-12-28 | 2025-04-11 | 浪潮云洲工业互联网有限公司 | A fish pond intelligent inspection system based on industrial interconnection |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118664622A (en) * | 2024-08-21 | 2024-09-20 | 中交机电工程局有限公司 | Unmanned track inspection robot |
| CN119814976A (en) * | 2024-12-28 | 2025-04-11 | 浪潮云洲工业互联网有限公司 | A fish pond intelligent inspection system based on industrial interconnection |
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