CN117053793A - Water quality monitoring and inspection method, device and equipment based on sea-air cross-domain cooperation - Google Patents
Water quality monitoring and inspection method, device and equipment based on sea-air cross-domain cooperation Download PDFInfo
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Abstract
The application relates to a water quality monitoring and inspection method, a device and equipment based on sea-air cross-domain cooperation, which are characterized in that a target monitoring area and a plurality of preset monitoring points are firstly obtained, then an unmanned aerial vehicle is controlled to perform preliminary detection on the target monitoring area to obtain a suspected pollution area, then a global monitoring path in the target monitoring area is obtained according to the position information of the suspected pollution area and the position information of the preset monitoring points, and finally an unmanned aerial vehicle is controlled to accurately detect the target monitoring area according to the global monitoring path to obtain a monitoring result of the target monitoring area. Compared with the prior art, the application provides a concrete scheme for carrying out water quality monitoring by cooperatively matching the unmanned aerial vehicle and the unmanned aerial vehicle, so that the advantages of two different devices are fully exerted, the unmanned aerial vehicle can accurately go to a destination for accurate detection, meaningless repeated cruising is avoided, the problem of unmanned aerial vehicle cruising debilitation is solved, and the detection efficiency is greatly improved.
Description
Technical Field
The application relates to the technical field of water quality monitoring, in particular to a water quality monitoring and inspection method, device and equipment based on sea-air cross-domain cooperation.
Background
Unmanned aerial vehicle, unmanned ship are as two kinds of unmanned vehicles that mobility is strong, and the flexibility is high, and both are used in each field gradually, wherein also include the water quality monitoring field. However, at present, most water quality monitoring mostly adopts unmanned aerial vehicle or unmanned ship monomer operation, unmanned aerial vehicle with poor endurance and unmanned ship with limited monitoring range are inevitably limited in monomer operation, and sampling monitoring under the conditions of full coverage of the area and complicated and changeable water areas is difficult to achieve.
Correspondingly, the research on the cooperation of the unmanned aerial vehicle and the unmanned aerial vehicle is mainly focused on the aspects of the co-combat and the cooperation landing of the unmanned aerial vehicle and the unmanned aerial vehicle, wherein a large amount of blank exists in the research on the water quality monitoring of the cooperation of the unmanned aerial vehicle and the unmanned aerial vehicle, the problem of full coverage of the water quality monitoring of an irregular area is not suitable for a scheme of the cooperation of the unmanned aerial vehicle and the unmanned aerial vehicle, and a high-efficiency inspection planning scheme of the cooperation of the unmanned aerial vehicle and the unmanned aerial vehicle is not suitable.
Therefore, a high-efficiency sea-air cross-domain collaborative water quality monitoring inspection scheme is needed.
Disclosure of Invention
In view of the above, it is necessary to provide a water quality monitoring and inspection method, device and equipment based on sea-air cross-domain cooperation, which are used for solving the problem of how to realize sea-air cross-domain cooperation water quality monitoring and inspection by using an unmanned aerial vehicle and an unmanned ship.
In order to achieve the technical purpose, the application adopts the following technical scheme:
in a first aspect, the application provides a water quality monitoring and inspection method based on sea-air cross-domain cooperation, which comprises the following steps:
acquiring a target monitoring area and a plurality of preset monitoring points in the target monitoring area;
the unmanned aerial vehicle is controlled to perform preliminary detection on the target monitoring area to obtain a suspected pollution area in the target monitoring area;
obtaining a global monitoring path in the target monitoring area according to the position information of the suspected pollution area and the position information of the preset monitoring point;
and controlling the unmanned ship to accurately detect the target monitoring area according to the global monitoring path to obtain a monitoring result of the target monitoring area.
Further, the obtaining the target monitoring area and the plurality of preset monitoring points in the target monitoring area includes:
acquiring a monitoring sea area range, and dividing the monitoring sea area range to obtain a plurality of subareas;
selecting a plurality of preset monitoring points in each subarea respectively;
obtaining a monitoring execution sequence according to the position relation of the sub-areas;
selecting a subarea which needs to be detected by the unmanned aerial vehicle at the current moment from the plurality of subareas as a target monitoring area based on the monitoring execution sequence, and determining a plurality of preset monitoring points in the target monitoring area;
the monitoring execution sequence is the arrangement sequence of a plurality of subareas.
Further, the sub-areas are divided according to communication distances between the unmanned aerial vehicle and the unmanned aerial vehicle, and the unmanned aerial vehicle are respectively kept in two adjacent sub-areas when being detected.
Further, the obtaining the global monitoring path in the target monitoring area according to the position information of the suspected pollution area and the position information of the preset monitoring point includes:
the positions of the suspected pollution areas and the positions of the preset monitoring points are taken as task monitoring points;
and calculating paths sequentially passing through all the task monitoring points based on a path planning algorithm to obtain the global monitoring path.
Further, the controlling unmanned aerial vehicle performs preliminary detection on the target monitoring area to obtain a suspected pollution area in the target monitoring area, including:
the unmanned aerial vehicle is controlled to perform preliminary detection on the target monitoring area, and a suspected pollution area and an obstacle in the target monitoring area are obtained;
the path planning algorithm-based path calculation method calculates paths sequentially passing through all task monitoring points to obtain the global monitoring path, and the method comprises the following steps:
calculating paths sequentially passing through all the task monitoring points based on a path planning algorithm to obtain the initial monitoring path;
calculating a local monitoring path between two adjacent task monitoring points in the initial monitoring path based on an obstacle avoidance algorithm according to the position information of the obstacle;
and optimizing the initial monitoring path according to the local monitoring path to obtain the global monitoring path.
Further, the calculating, based on the path planning algorithm, paths sequentially passing through all the task monitoring points to obtain the initial monitoring path includes:
establishing a solution space based on all possible paths between the task monitoring points according to the task monitoring points;
according to the environmental parameters, unmanned plane parameters and unmanned ship parameters, establishing a pheromone evaluation model, wherein the pheromone is used for representing the cost between two task monitoring points;
initializing a plurality of ants in the solution space, the position of each ant in the solution space representing one possible path;
and according to the pheromone evaluation model, iteratively optimizing the position of ants in the solution space to obtain the initial monitoring path.
Further, the calculating, according to the position information of the obstacle, a local monitoring path between two adjacent task monitoring points in the initial monitoring path based on an obstacle avoidance algorithm includes:
obtaining two adjacent task monitoring points according to the initial monitoring path;
establishing a target solving space according to the space range between two adjacent task monitoring points;
taking one task monitoring point as a root node, establishing a rapid exploration random tree in the target solving space, wherein leaf nodes of the rapid exploration random tree randomly expand in the target solving space and avoid the obstacle, and the rapid exploration random tree comprises one leaf node reaching the other task monitoring point;
and obtaining the local monitoring path according to the rapid exploration random tree.
In a second aspect, the application also provides a water quality monitoring and inspection device based on sea-air cross-domain cooperation, which comprises:
the parameter initialization module is used for acquiring a target monitoring area and a plurality of preset monitoring points in the target monitoring area;
the unmanned aerial vehicle control module is used for controlling the unmanned aerial vehicle to perform preliminary detection on the target monitoring area to obtain a suspected pollution area in the target monitoring area;
the path planning module is used for obtaining a global monitoring path in the target monitoring area according to the position information of the suspected pollution area and the position information of the preset monitoring point;
and the unmanned ship control module is used for controlling the unmanned ship to accurately detect the target monitoring area according to the global monitoring path to obtain a monitoring result of the target monitoring area.
In a third aspect, the application also provides an electronic device comprising a memory and a processor, wherein,
a memory for storing a program;
and the processor is coupled with the memory and is used for executing the program stored in the memory so as to realize the steps in the water quality monitoring and inspection method based on sea-air cross-domain cooperation in any one of the implementation modes.
In a fourth aspect, the present application further provides a computer readable storage medium, configured to store a computer readable program or instruction, where the program or instruction, when executed by a processor, is capable of implementing the steps in the water quality monitoring and inspection method based on sea-air cross-domain collaboration in any one of the above implementation manners.
The application provides a water quality monitoring and inspection method, device and equipment based on sea-air cross-domain cooperation, which are characterized in that a target monitoring area and a plurality of preset monitoring points in the target monitoring area are firstly obtained, then an unmanned aerial vehicle is controlled to perform preliminary detection on the target monitoring area to obtain a suspected pollution area in the target monitoring area, then a global monitoring path in the target monitoring area is obtained according to the position information of the suspected pollution area and the position information of the preset monitoring points, and finally the unmanned aerial vehicle is controlled to accurately detect the target monitoring area according to the global monitoring path to obtain the monitoring result of the target monitoring area. Compared with the prior art, the application provides a concrete scheme for carrying out water quality monitoring by cooperatively matching the unmanned aerial vehicle and the unmanned aerial vehicle, so that the advantages of two different devices are fully exerted, the unmanned aerial vehicle can accurately go to a destination for accurate detection, meaningless repeated cruising is avoided, the problem of unmanned aerial vehicle cruising debilitation is solved, and the detection efficiency is greatly improved.
Drawings
FIG. 1 is a flow chart of a method for monitoring and patrolling an embodiment of water quality based on sea-air cross-domain cooperation provided by the application;
FIG. 2 is a flowchart of a method according to an embodiment of step S103 in FIG. 1;
FIG. 3 is a flowchart illustrating a method according to an embodiment of step S202 in FIG. 2;
FIG. 4 is a schematic structural diagram of an embodiment of a water quality monitoring and inspection device based on sea-air cross-domain cooperation provided by the application;
fig. 5 is a schematic structural diagram of an embodiment of an electronic device according to the present application.
Detailed Description
The following detailed description of preferred embodiments of the application is made in connection with the accompanying drawings, which form a part hereof, and together with the description of the embodiments of the application, are used to explain the principles of the application and are not intended to limit the scope of the application.
It is to be understood that technical terms, acronyms, and the like appearing hereinafter are prior art and those skilled in the art are able to understand their meanings based on context and are not described here too much for reasons of brevity.
In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The application provides a water quality monitoring and inspection method, device, equipment and storage medium based on sea-air cross-domain cooperation, which are respectively described below.
Referring to fig. 1, the application discloses a water quality monitoring and inspection method based on sea-air cross-domain cooperation, which comprises the following steps:
s101, acquiring a target monitoring area and a plurality of preset monitoring points in the target monitoring area;
s102, controlling the unmanned aerial vehicle to perform preliminary detection on the target monitoring area to obtain a suspected pollution area in the target monitoring area;
s103, obtaining a global monitoring path in the target monitoring area according to the position information of the suspected pollution area and the position information of the preset monitoring point;
s104, controlling the unmanned ship to accurately detect the target monitoring area according to the global monitoring path, and obtaining a monitoring result of the target monitoring area.
Compared with the prior art, the application provides a concrete scheme for carrying out water quality monitoring by cooperatively matching the unmanned aerial vehicle and the unmanned aerial vehicle, so that the advantages of two different devices are fully exerted, the unmanned aerial vehicle can accurately go to a destination for accurate detection, meaningless repeated cruising is avoided, the problem of unmanned aerial vehicle cruising debilitation is solved, and the detection efficiency is greatly improved.
Specifically, in a preferred embodiment, the step S101 of acquiring the target monitoring area and a plurality of preset monitoring points in the target monitoring area includes:
acquiring a monitoring sea area range, and dividing the monitoring sea area range to obtain a plurality of subareas;
selecting a plurality of preset monitoring points in each subarea respectively;
obtaining a monitoring execution sequence according to the position relation of the sub-areas;
selecting a subarea which needs to be detected by the unmanned aerial vehicle at the current moment from the plurality of subareas as a target monitoring area based on the monitoring execution sequence, and determining a plurality of preset monitoring points in the target monitoring area;
the monitoring execution sequence is the arrangement sequence of a plurality of subareas.
The unmanned aerial vehicle and the unmanned aerial vehicle sequentially detect the plurality of subareas according to the monitoring execution sequence, and the subareas which the unmanned aerial vehicle needs to detect at the current moment are the target detection areas.
Further, in a preferred embodiment, a plurality of said sub-areas are divided according to the communication distance between said unmanned aerial vehicle and said unmanned aerial vehicle, which are kept in two adjacent sub-areas when detecting.
In the step S102, the unmanned aerial vehicle is controlled to perform preliminary detection on the target monitoring area to obtain a suspected pollution area in the target monitoring area, and the specific implementation manner of the method is the prior art, which is not described herein too much.
Further, in a preferred embodiment, the step S102 further includes:
and controlling the unmanned aerial vehicle to perform preliminary detection on the target monitoring area to obtain a suspected pollution area and an obstacle in the target monitoring area, wherein other information such as the obstacle can be detected besides the suspected pollution area during preliminary detection of the unmanned aerial vehicle.
Further, referring to fig. 2, in a preferred embodiment, step S103, obtaining the global monitoring path in the target monitoring area according to the position information of the suspected pollution area and the position information of the preset monitoring point includes:
s201, taking the positions of the suspected pollution areas and the positions of the preset monitoring points as task monitoring points;
s202, calculating paths sequentially passing through all task monitoring points based on a path planning algorithm to obtain the global monitoring path.
It may be appreciated that in step S201, when the preliminary detection result of the unmanned aerial vehicle does not include the suspected pollution area, only the preset detection point may be used as the task monitoring point.
Further, referring to fig. 3, in a preferred embodiment, the step S202 of calculating, based on the path planning algorithm, paths sequentially passing through all the task monitoring points to obtain the global monitoring path specifically includes:
s301, calculating paths sequentially passing through all task monitoring points based on a path planning algorithm to obtain the initial monitoring path;
s302, calculating a local monitoring path between two adjacent task monitoring points in the initial monitoring path based on an obstacle avoidance algorithm according to the position information of the obstacle;
s303, optimizing the initial monitoring path according to the local monitoring path to obtain the global monitoring path.
The obtained initial detection path is optimized through the obstacle avoidance algorithm, so that obstacle avoidance in the unmanned ship operation process is realized. It will be appreciated that the initial detection path may be taken directly as the final desired global detection path when no obstacle is present.
Specifically, in a preferred embodiment, in step S301, the path planning algorithm calculates paths sequentially passing through all the task monitoring points to obtain the initial monitoring path, where an ant colony algorithm is used for path planning, and the specific process includes:
establishing a solution space based on all possible paths between the task monitoring points according to the task monitoring points;
according to the environmental parameters, unmanned plane parameters and unmanned ship parameters, establishing a pheromone evaluation model, wherein the pheromone is used for representing the cost between two task monitoring points;
initializing a plurality of ants in the solution space, the position of each ant in the solution space representing one possible path;
and according to the pheromone evaluation model, iteratively optimizing the position of ants in the solution space to obtain the initial monitoring path.
Further, in a preferred embodiment, in step S302, according to the position information of the obstacle, a local monitoring path between two adjacent task monitoring points in the initial monitoring path is calculated based on an obstacle avoidance algorithm, where the step of performing the obstacle avoidance path planning by using an RRT algorithm includes:
obtaining two adjacent task monitoring points according to the initial monitoring path;
establishing a target solving space according to the space range between two adjacent task monitoring points;
taking one task monitoring point as a root node, establishing a rapid exploration random tree in the target solving space, wherein leaf nodes of the rapid exploration random tree randomly expand in the target solving space and avoid the obstacle, and the rapid exploration random tree comprises one leaf node reaching the other task monitoring point;
and obtaining the local monitoring path according to the rapid exploration random tree.
The present application also provides a more detailed embodiment for more clearly describing the above steps S101 to S104:
after determining the scope of the monitoring sea area, firstly dividing the monitoring sea area into a plurality of subareas, planning the monitoring execution sequence of the subareas according to the monitoring task (namely, the sequence of the unmanned aerial vehicle and the unmanned ship for sequentially executing the detection), entering the next subarea to execute the task according to the monitoring execution sequence after the subareas where the unmanned aerial vehicle and the unmanned ship are respectively located independently complete the monitoring task, and setting a plurality of preset monitoring points in the subareas when the subareas which are required to be detected currently are target monitoring areas during each detection.
The preset monitoring points in the subareas can be set by any method, including random setting and selection according to a certain rule, and in the embodiment, two modes of setting can be adopted, namely, default autonomous coverage type preset monitoring points and custom type preset monitoring points. The autonomous coverage is as follows: for each sub-area, selecting tetrad points of key lines of each vertex and the polygon of the sub-area as preset monitoring points; the custom-made monitoring points are manually selected according to actual conditions, and the custom-made planning can be performed aiming at key monitoring points such as a drain outlet, a river sink inlet and the like of a water area.
In the detection process, the unmanned aerial vehicle and the unmanned ship must cooperate to execute the cruising task according to the planned monitoring execution sequence, namely, the unmanned aerial vehicle firstly carries out the sweep cruising in the subarea air, the carried multispectral camera is utilized to shoot the condition of the water area, the spectral image is transmitted back to the shore-based platform in real time, the shore-based platform carries out the water quality analysis of the monitored water area according to the spectral image and carries out the preliminary judgment of the suspected pollution area and the obstacle, and meanwhile, the position information of the suspected pollution area and the obstacle is transmitted to the unmanned ship. In the process, each sub-area must be complied with the monitoring sequence of the unmanned aerial vehicle preliminary monitoring first and then the unmanned aerial vehicle accurate monitoring. In addition, in order to ensure that the unmanned aerial vehicle and the unmanned aerial vehicle can always keep communication, the division of the sub-region in the embodiment refers to the communication distance between the unmanned aerial vehicle and the unmanned aerial vehicle, and the unmanned aerial vehicle always keep in two adjacent sub-regions during collaborative monitoring.
When communication is limited due to the fact that the unmanned aerial vehicle and the unmanned aerial vehicle operate in the front and rear sub-areas which are not adjacent, the unmanned aerial vehicle can be controlled to hover or decelerate to wait for the unmanned aerial vehicle to keep communication smooth, and the unmanned aerial vehicle keep to carry out collaborative monitoring operation within a certain distance. Further, for a collaborative idle gear period when the unmanned aerial vehicle is charged, the unmanned aerial vehicle carries out coverage monitoring on the subareas when the unmanned aerial vehicle stops working until the unmanned aerial vehicle is charged, the unmanned aerial vehicle starts in advance and begins collaborative working again according to the planned subarea monitoring execution sequence.
When the unmanned aerial vehicle completes the inspection task of the subarea, and the unmanned aerial vehicle completes the preliminary monitoring of the subarea, the unmanned aerial vehicle automatically goes to the suspected pollution area to execute the accurate monitoring task according to the suspected pollution area, the operation route and the monitoring task point of the next subarea in the monitoring execution sequence, and simultaneously the unmanned aerial vehicle automatically goes to the next subarea to execute the monitoring task according to the monitoring execution sequence. When unmanned aerial vehicle electricity is less than the setting, unmanned aerial vehicle can get back to and charge on the unmanned aerial vehicle, covers the monitoring by unmanned aerial vehicle to the subregion when unmanned aerial vehicle stops the operation.
In the running process of the unmanned ship, a preset monitoring point and a suspected pollution area are used as monitoring task points (when the unmanned ship is in a pollution-like area, the preset monitoring point is only used as the monitoring task points), the unmanned ship is planned according to the positions of the monitoring task points based on an ant colony algorithm or a self-organizing mapping network algorithm (other path planning algorithms can be adopted in practice), global monitoring paths passing through each monitoring task point in sequence in a subarea are planned, and between the two monitoring task points, the unmanned ship is planned according to the position information of an obstacle by adopting an RRT algorithm (other obstacle avoidance algorithms can be adopted in practice), so that obstacle avoidance is realized.
The process of finding the optimal path by the ant colony algorithm can be summarized as the following steps:
1. initializing pheromones:
before the algorithm starts, the pheromone concentration needs to be initialized. In general, the pheromone concentration on all sides can be initialized to a small positive number, representing the pheromone left by the ant on each path.
2. Random initialization of ants:
each ant is randomly assigned a starting node and placed at the starting node.
3. And (3) movement of ants:
each ant selects the next node to move according to a certain probability function. A common probability function is roulette selection or maximum. Ants will choose based on the pheromone concentration of the current node and heuristic information (e.g., distance). During the movement, ants will leave pheromones to indicate whether the path is good or bad.
4. Searching of the solution space:
all ants were moved according to the probability function until each ant reached the endpoint. During the search, ants will choose nodes of potentially better paths to move based on the informative guides and heuristics. Ants can exchange information with each other.
5. Updating the pheromone:
when all ants reach the end point, the pheromone concentration is updated according to the quality of the path. The current pheromone is usually attenuated, plus the new pheromone left by the ant. The path quality is typically determined by an objective function, such as path length. The pheromone concentration on the preferred path increases, while the pheromone concentration on the worse path decreases.
6. Judging a termination condition:
judging according to a preset termination condition, for example, limiting the iteration times or finding a solution meeting a specific condition.
7. Repeating the iteration:
if the termination condition is not satisfied, steps 3 to 6 are repeatedly performed to continue searching for a more optimal path. Each iteration is updated with the pheromone of the last iteration, and the moving path of ants may also change.
By constantly iterating and updating the pheromone, the ant colony algorithm can gradually converge to the optimal path. Ants tend to choose paths with high pheromone concentration, but also have some exploratory properties, so that it is possible to find solutions that are globally optimal or near optimal.
In this embodiment, the positions of the monitored task points are known, and a solution space is established by using the position information of the possible suspected pollution area obtained by the unmanned aerial vehicle, and then a pheromone evaluation model is established according to environmental parameters (water temperature, wave fluctuation degree, resistance, etc., default values can be set manually), unmanned aerial vehicle parameters (driving capability, running speed, fuel consumption speed, etc. of the unmanned aerial vehicle) and unmanned ship parameters (driving capability, running speed, fuel consumption speed, etc. of the unmanned ship), and the pheromone is used for representing the cost between two task monitoring points. Then, a plurality of ants are randomly initialized in a solution space, parameters such as track persistence 1- ρ, relative importance alpha, visibility relative importance beta, iteration times t, ant population number m and the like in the process of solving the optimal path are set, and in the process of solving the embodiment, a probability function for selecting a destination (namely the optimal path of the unmanned ship in the problem) based on the kth ant is as follows:
in this embodiment, the pheromone concentration update formula in the pheromone model:
τ ij (t+n)＝(1-ρ)·τ ij (t)+Δτ ij (t)
wherein Deltaτ ij k (i) For the concentration of pheromone released by the kth ant on the i and j link paths, wherein eta ij is the visibility between the i and j monitoring task points, allowk is the set of nodes (nodes are possible paths) which are not accessed by the kth ant, and tau ij (t) the intensity of the pheromone from the monitoring task point i to the monitoring task point j when the current iteration times t are shown;
the optimal global monitoring path in the current subarea can be obtained by solving and calculating the positions of a plurality of ants according to the pheromones (obtained by the pheromone evaluation model) mastered by each ant.
In another preferred embodiment, the path of the unmanned ship global monitoring path adopts a self-organizing map network algorithm, namely an SOM algorithm, firstly, an SOM network is built according to a monitoring sea area environment map, neurons in the SOM network respectively correspond to different monitoring task point positions on the map, then, position information in a target monitoring sea area is converted into vector representation, and the vector representation is input into the network after normalization. And then, continuously adjusting the weights of the neurons by using a competition learning algorithm to obtain a mapping space between the neurons, attenuating the updated amplitude by adopting a moving average method when the weights are updated to ensure the stability of the algorithm, and finally searching an optimal path by using a search algorithm to obtain a global monitoring path.
If no obstacle exists in the subarea, the unmanned ship can carry out cruising inspection according to the global monitoring path, if the obstacle exists in the subarea, the unmanned ship can plan the local monitoring path between two monitoring task points by adopting an RRT algorithm according to the position information of the obstacle, and optimize the global monitoring path as an initial monitoring path through the local monitoring path, so that an optimized global monitoring path is finally obtained, and obstacle avoidance is carried out.
The RRT (Rapidly-exploring Random Tree) algorithm is an algorithm for path planning, which can be used to avoid obstacles, and explores a random tree quickly.
The obstacle avoidance process of the RRT algorithm is as follows:
1. initializing:
first, it is necessary to define a start point and a target point, and take the start point as a root node of the tree. It is also necessary to determine the position and shape of the obstacle.
2. Adding nodes:
a new node is generated by generating random points or selecting points according to a certain rule. This node will be added to the tree. To explore the unknown space, new nodes may fall outside the generated tree.
3. Searching for a nearest neighbor node:
the node closest to the new node is found from the generated tree. The node closest to the new node is found by calculating the euclidean distance or other distance metric.
4. Expanding the tree:
according to certain rules, an edge is added from the nearest neighbor node to the new node. This allows the tree to be extended and the new node to be a child of the nearest neighbor.
5. Obstacle avoidance detection:
in the process of expanding the tree, collision detection needs to be performed on the new node and the obstacle. If a new node collides with an obstacle, the node is discarded, and a new node is regenerated.
6. Judging termination conditions:
it is checked whether the new node is close to the target point. If the target point is sufficiently close, the algorithm may be stopped and the final path returned. Otherwise, returning to the step 2 and continuing to generate new nodes.
7. Iteration:
and (3) continuously repeating the steps 2 to 6 until a path connecting the starting point and the target point is found.
Eventually, the RRT algorithm will generate a tree with its leaf nodes near the target point and as far as possible avoiding the obstacle. The optimal path may be selected or post-processed on the generated tree to obtain the final path, as desired. The RRT algorithm has the capability of quickly exploring an unknown space while taking obstacle avoidance into consideration.
In this embodiment, the course of the RRT algorithm is continuously calculated during the unmanned ship moving process, a point is randomly sampled from the target space (i.e., the area space between two monitoring task points) at each time step (i.e., each expansion of the tree), then the random tree is expanded, the course reaching the destination is continuously calculated until the motion track is generated, whether the path can reach the destination is judged, and finally the obstacle avoidance path is obtained, i.e., the local monitoring path row between two monitoring task points is obtained.
Meanwhile, in the embodiment, an improved integral LOS guidance algorithm is also used for eliminating attitude errors in the unmanned ship advancing process, and the obtained expected speed and expected heading angle are converted into PWM signals for controlling the navigation speed and direction. Wherein the neural network PID is used to improve the dynamic response index.
After the unmanned ship reaches the suspected pollution area, the unmanned ship collects and analyzes the water sample in the area by using the carried water quality monitoring assembly, and compares the analysis result with the water quality parameter information in the database; when the water quality parameter exceeds the standard, the unmanned ship reserves the polluted water sample in the water area and then further executes the task.
In the embodiment, the water quality monitoring assembly uses STM32F103R6 as a main control chip, a plurality of sensor probes sensitive to different water quality indexes are installed, the sensor probes are connected in a detection circuit, the output result is a 4-20mA current signal, the output is carried out after A/D conversion, and 6A/D channels are used for respectively corresponding to 6 main water quality indexes including water temperature, pH value, dissolved oxygen, turbidity, oxidation-reduction potential and ammonia nitrogen. In order to ensure that the accuracy of data acquired by the water quality sensor is high enough, the embodiment adopts a precise I/V conversion special integrated circuit composed of RCV420, adopts industrial-grade temperature of minus 25 ℃ to plus 85 ℃, and converts 4-20mA current into 0-5V voltage for A/D conversion.
Unmanned ship and unmanned aerial vehicle have compensatied defect and the blind area of monomer operation through inseparable collaborative operation each other, unmanned aerial vehicle's take-off and land platform for unmanned aerial vehicle power supply has solved unmanned aerial vehicle's continuation of journey problem, has compensatied unmanned aerial vehicle's the monitoring efficiency of charging idle shelves period simultaneously, has realized intelligent, dynamic real-time supervision, has improved the efficiency of monitoring greatly.
The embodiment has the following effects and advantages:
1. the water quality monitoring and inspection method and system based on sea-air cross-domain cooperation fully combine the advantages of the unmanned aerial vehicle and the unmanned ship, and make up the defects of poor cruising and monitoring endurance, limited monitoring range and the like of single operation;
2. the method has the advantages that a cruising mode of cooperation of the method and the device is designed, an optimal detection route is obtained by re-planning the monitoring points and cruising paths, the problem of operation weakness in a cooperative neutral period of unmanned aerial vehicle charging is solved, the defects of small operation range, single operation and the like of a traditional inspection mode are overcome, and full coverage of monitoring inspection is efficiently realized.
In order to better implement the water quality monitoring and inspection method based on the sea-air cross-domain cooperation in the embodiment of the present application, referring to fig. 4 correspondingly, fig. 4 is a schematic structural diagram of an embodiment of the water quality monitoring and inspection device based on the sea-air cross-domain cooperation, where the water quality monitoring and inspection device 400 based on the sea-air cross-domain cooperation provided by the embodiment of the present application includes:
a parameter initialization module 410, configured to obtain a target monitoring area and a plurality of preset monitoring points in the target monitoring area;
the unmanned aerial vehicle control module 420 is configured to control an unmanned aerial vehicle to perform preliminary detection on the target monitoring area, so as to obtain a suspected pollution area in the target monitoring area;
the path planning module 430 is configured to obtain a global monitoring path in the target monitoring area according to the position information of the suspected pollution area and the position information of the preset monitoring point;
and the unmanned ship control module 440 is configured to control the unmanned ship to accurately detect the target monitoring area according to the global monitoring path, so as to obtain a monitoring result of the target monitoring area.
What needs to be explained here is: the corresponding apparatus 400 provided in the foregoing embodiments may implement the technical solutions described in the foregoing method embodiments, and the specific implementation principles of the foregoing modules or units may be referred to the corresponding content in the foregoing method embodiments, which is not repeated herein.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the application. Based on the above-mentioned water quality monitoring and inspection method based on the sea-air cross-domain cooperation, the application also correspondingly provides a water quality monitoring and inspection device 500 based on the sea-air cross-domain cooperation, namely the above-mentioned electronic device, and the water quality monitoring and inspection device 500 based on the sea-air cross-domain cooperation can be a mobile terminal, a desktop computer, a notebook computer, a palm computer, a server and other computing devices. The water quality monitoring and inspection device 500 based on the sea-air cross-domain cooperation comprises a processor 510, a memory 520 and a display 530. Fig. 5 shows only a portion of the components of the water quality monitoring and inspection device based on sea-air cross-domain collaboration, but it should be understood that not all of the illustrated components need be implemented, and that more or fewer components may alternatively be implemented.
The memory 520 may in some embodiments be an internal storage unit of the water quality monitoring and inspection device 500 based on the sea-air-cross-domain cooperation, for example, a hard disk or a memory of the water quality monitoring and inspection device 500 based on the sea-air-cross-domain cooperation. The memory 520 may also be an external storage device of the water quality monitoring and inspection device 500 based on the sea-air-cross-domain cooperation, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are equipped on the water quality monitoring and inspection device 500 based on the sea-air-cross-domain cooperation. Further, the memory 520 may also include both internal and external storage units of the water quality monitoring and inspection device 500 based on sea-air cross-domain collaboration. The memory 520 is used for storing application software and various data installed in the water quality monitoring and inspection device 500 based on the sea-air cross-domain cooperation, for example, program codes for installing the water quality monitoring and inspection device 500 based on the sea-air cross-domain cooperation. The memory 520 may also be used to temporarily store data that has been output or is to be output. In an embodiment, the memory 520 stores a water quality monitoring and inspection program 540 based on the sea-air-cross-domain cooperation, and the water quality monitoring and inspection program 540 based on the sea-air-cross-domain cooperation can be executed by the processor 510, so as to implement the water quality monitoring and inspection method based on the sea-air-cross-domain cooperation according to the embodiments of the present application.
The processor 510 may be, in some embodiments, a central processing unit (Central Processing Unit, CPU), microprocessor or other data processing chip for executing program codes or processing data stored in the memory 520, for example, performing a water quality monitoring and inspection method based on air-sea cross-domain coordination, etc.
The display 530 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like in some embodiments. The display 530 is used for displaying information at the sea-air based cross-domain collaboration water quality monitoring and inspection device 500 and for displaying a visual user interface. The components 510-530 of the water quality monitoring and inspection device 500 based on the sea-air cross-domain cooperation communicate with each other through a system bus.
In one embodiment, the steps in the above water quality monitoring and inspection method based on the sea-air-cross-domain cooperation are implemented when the processor 510 executes the water quality monitoring and inspection program 540 based on the sea-air-cross-domain cooperation in the memory 520.
The embodiment also provides a computer readable storage medium, on which a water quality monitoring and inspection program based on the sea-air cross-domain cooperation is stored, and the steps in the above embodiments can be implemented when the water quality monitoring and inspection program based on the sea-air cross-domain cooperation is executed by a processor.
The application provides a water quality monitoring and inspection method, device and equipment based on sea-air cross-domain cooperation, which are characterized in that a target monitoring area and a plurality of preset monitoring points in the target monitoring area are firstly obtained, then an unmanned aerial vehicle is controlled to perform preliminary detection on the target monitoring area to obtain a suspected pollution area in the target monitoring area, then a global monitoring path in the target monitoring area is obtained according to the position information of the suspected pollution area and the position information of the preset monitoring points, and finally the unmanned aerial vehicle is controlled to accurately detect the target monitoring area according to the global monitoring path to obtain the monitoring result of the target monitoring area. Compared with the prior art, the application provides a concrete scheme for carrying out water quality monitoring by cooperatively matching the unmanned aerial vehicle and the unmanned aerial vehicle, so that the advantages of two different devices are fully exerted, the unmanned aerial vehicle can accurately go to a destination for accurate detection, meaningless repeated cruising is avoided, the problem of unmanned aerial vehicle cruising debilitation is solved, and the detection efficiency is greatly improved.
The present application is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present application are intended to be included in the scope of the present application.
Claims (10)
1. A water quality monitoring and inspection method based on sea-air cross-domain cooperation is characterized by comprising the following steps:
acquiring a target monitoring area and a plurality of preset monitoring points in the target monitoring area;
the unmanned aerial vehicle is controlled to perform preliminary detection on the target monitoring area to obtain a suspected pollution area in the target monitoring area;
obtaining a global monitoring path in the target monitoring area according to the position information of the suspected pollution area and the position information of the preset monitoring point;
and controlling the unmanned ship to accurately detect the target monitoring area according to the global monitoring path to obtain a monitoring result of the target monitoring area.
2. The water quality monitoring and inspection method based on sea-air cross-domain cooperation according to claim 1, wherein the obtaining a target monitoring area and a plurality of preset monitoring points in the target monitoring area comprises:
acquiring a monitoring sea area range, and dividing the monitoring sea area range to obtain a plurality of subareas;
selecting a plurality of preset monitoring points in each subarea respectively;
obtaining a monitoring execution sequence according to the position relation of the sub-areas;
selecting a subarea which needs to be detected by the unmanned aerial vehicle at the current moment from the plurality of subareas as a target monitoring area based on the monitoring execution sequence, and determining a plurality of preset monitoring points in the target monitoring area;
the monitoring execution sequence is the arrangement sequence of a plurality of subareas.
3. The water quality monitoring and inspection method based on sea-air cross-domain cooperation according to claim 2, wherein a plurality of the subareas are divided according to communication distances between the unmanned aerial vehicle and the unmanned aerial vehicle, and the unmanned aerial vehicle are respectively kept in two adjacent subareas when being detected.
4. The water quality monitoring and inspection method based on sea-air cross-domain cooperation according to claim 1, wherein the obtaining the global monitoring path in the target monitoring area according to the position information of the suspected pollution area and the position information of the preset monitoring point comprises:
the positions of the suspected pollution areas and the positions of the preset monitoring points are taken as task monitoring points;
and calculating paths sequentially passing through all the task monitoring points based on a path planning algorithm to obtain the global monitoring path.
5. The water quality monitoring and inspection method based on sea-air cross-domain cooperation of claim 4, wherein the controlling unmanned aerial vehicle performs preliminary detection on the target monitoring area to obtain a suspected pollution area in the target monitoring area, and the method comprises the following steps:
the unmanned aerial vehicle is controlled to perform preliminary detection on the target monitoring area, and a suspected pollution area and an obstacle in the target monitoring area are obtained;
the path planning algorithm-based path calculation method calculates paths sequentially passing through all task monitoring points to obtain the global monitoring path, and the method comprises the following steps:
calculating paths sequentially passing through all the task monitoring points based on a path planning algorithm to obtain the initial monitoring path;
calculating a local monitoring path between two adjacent task monitoring points in the initial monitoring path based on an obstacle avoidance algorithm according to the position information of the obstacle;
and optimizing the initial monitoring path according to the local monitoring path to obtain the global monitoring path.
6. The water quality monitoring and inspection method based on sea-air cross-domain cooperation according to claim 5, wherein the path planning algorithm calculates paths sequentially passing through all task monitoring points to obtain the initial monitoring path, and the method comprises the following steps:
establishing a solution space based on all possible paths between the task monitoring points according to the task monitoring points;
according to the environmental parameters, unmanned plane parameters and unmanned ship parameters, establishing a pheromone evaluation model, wherein the pheromone is used for representing the cost between two task monitoring points;
initializing a plurality of ants in the solution space, the position of each ant in the solution space representing one possible path;
and according to the pheromone evaluation model, iteratively optimizing the position of ants in the solution space to obtain the initial monitoring path.
7. The water quality monitoring and inspection method based on sea-air cross-domain cooperation according to claim 5, wherein the calculating a local monitoring path between two adjacent task monitoring points in the initial monitoring path based on an obstacle avoidance algorithm according to the position information of the obstacle comprises:
obtaining two adjacent task monitoring points according to the initial monitoring path;
establishing a target solving space according to the space range between two adjacent task monitoring points;
taking one task monitoring point as a root node, establishing a rapid exploration random tree in the target solving space, wherein leaf nodes of the rapid exploration random tree randomly expand in the target solving space and avoid the obstacle, and the rapid exploration random tree comprises one leaf node reaching the other task monitoring point;
and obtaining the local monitoring path according to the rapid exploration random tree.
8. Water quality monitoring inspection device based on sea-air cross-domain cooperation, which is characterized by comprising:
the parameter initialization module is used for acquiring a target monitoring area and a plurality of preset monitoring points in the target monitoring area;
the unmanned aerial vehicle control module is used for controlling the unmanned aerial vehicle to perform preliminary detection on the target monitoring area to obtain a suspected pollution area in the target monitoring area;
the path planning module is used for obtaining a global monitoring path in the target monitoring area according to the position information of the suspected pollution area and the position information of the preset monitoring point;
and the unmanned ship control module is used for controlling the unmanned ship to accurately detect the target monitoring area according to the global monitoring path to obtain a monitoring result of the target monitoring area.
9. An electronic device comprising a memory and a processor, wherein,
the memory is used for storing programs;
the processor is coupled to the memory and is configured to execute the program stored in the memory, so as to implement the steps in the water quality monitoring and inspection method based on sea-air cross-domain cooperation as set forth in any one of claims 1 to 7.
10. A computer readable storage medium storing a computer readable program or instructions which, when executed by a processor, enable the implementation of the steps in the water quality monitoring and patrol method based on sea-air cross-domain collaboration as claimed in any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117274827A (en) * | 2023-11-23 | 2023-12-22 | 江苏国态环保集团有限公司 | Intelligent environment-friendly remote real-time monitoring and early warning method and system |
| CN117387628A (en) * | 2023-12-11 | 2024-01-12 | 深圳大学 | Underwater robot path planning method and device based on directed particle ant colony |
| CN117687416A (en) * | 2024-01-25 | 2024-03-12 | 水利部交通运输部国家能源局南京水利科学研究院 | Path planning method and system for river network water safety detection device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117274827A (en) * | 2023-11-23 | 2023-12-22 | 江苏国态环保集团有限公司 | Intelligent environment-friendly remote real-time monitoring and early warning method and system |
| CN117274827B (en) * | 2023-11-23 | 2024-02-02 | 江苏国态环保集团有限公司 | Intelligent environment-friendly remote real-time monitoring and early warning method and system |
| CN117387628A (en) * | 2023-12-11 | 2024-01-12 | 深圳大学 | Underwater robot path planning method and device based on directed particle ant colony |
| CN117387628B (en) * | 2023-12-11 | 2024-02-23 | 深圳大学 | Underwater robot path planning method and device based on directed particle ant colony |
| CN117687416A (en) * | 2024-01-25 | 2024-03-12 | 水利部交通运输部国家能源局南京水利科学研究院 | Path planning method and system for river network water safety detection device |
| CN117687416B (en) * | 2024-01-25 | 2024-04-12 | 水利部交通运输部国家能源局南京水利科学研究院 | Path planning method and system for river network water safety detection device |
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