CN113596730A - Multi-event monitoring data acquisition method and system, unmanned aerial vehicle device and medium - Google Patents
Multi-event monitoring data acquisition method and system, unmanned aerial vehicle device and medium Download PDFInfo
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Abstract
The invention provides a data acquisition method, a data acquisition system, unmanned aerial vehicle equipment and a medium for multi-event monitoring, wherein the method comprises the following steps of; acquiring the priority of an event and an event occurrence area, classifying the event according to the priority, mapping a classification result to the event occurrence area, and determining a monitoring area of the event; determining data collection area position information and management node position information according to the monitoring area; gathering position information of the data collection area and position information of the management node, and determining a constraint condition of data collection time of the unmanned aerial vehicle; optimizing the flight path of the unmanned aerial vehicle according to the time priority contained in the monitoring area and the constraint condition of the data acquisition time of the unmanned aerial vehicle; controlling the unmanned aerial vehicle to acquire data according to the optimized flight path of the unmanned aerial vehicle; the method can effectively improve the data value density, has higher efficiency and is more flexible, meets the requirement and the target of rapid monitoring of multiple events and multiple regions, and can be widely applied to the technical field of remote monitoring of unmanned aerial vehicles.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle remote monitoring, in particular to a data acquisition method and system for multi-event monitoring, unmanned aerial vehicle equipment and a medium.
Background
In recent years, with the continuous and deep research on technologies such as internet of things, cooperative sensing, wireless communication and the like, the sensing internet of things is widely applied to monitoring and monitoring of emergent events such as public safety, environment and the like; becomes an important technology and method in the fields of intelligent monitoring, intelligent agriculture, intelligent cities and the like. Meanwhile, due to the flexibility of the unmanned aerial vehicle, the unmanned aerial vehicle is widely introduced into the data acquisition system of the Internet of things and becomes a key component of the whole system, and the introduction of the technology widens the range and effectiveness of the traditional data acquisition of the Internet of things.
Most of the existing unmanned aerial vehicle-assisted Internet of things data acquisition methods are based on the acquisition of all task data, and the shortest flight path of the unmanned aerial vehicle is used as a single target for relevant optimization during data acquisition; on one hand, the method has the defects of low data acquisition efficiency, low value density of collected data information, poor real-time performance, no consideration of task priority and the like; on the other hand, because the unmanned aerial vehicle mostly performs flight path optimization with the shortest path, the overall planning and the integrated utilization of the monitoring area and the monitoring event information are lacked.
Disclosure of Invention
In view of the above, to at least partially solve one of the above technical problems, an embodiment of the present invention is to provide a data collection method for multi-event monitoring, which is more efficient, has high information value density, has better real-time performance, and can implement overall planning and integrated utilization of monitoring area and monitoring event information, and a system, an unmanned aerial vehicle device, and a computer storage medium capable of implementing the method correspondingly.
In a first aspect, a technical solution of the present application provides a data acquisition method for multiple event monitoring, which includes the steps of:
acquiring the priority of an event and an event occurrence area, classifying the event according to the priority, mapping a classification result to the event occurrence area, and determining a monitoring area of the event;
determining data collection area position information and management node position information according to the monitoring area;
converging the position information of the data collection area and the position information of the management node, and determining a constraint condition of data collection time of the unmanned aerial vehicle;
optimizing the flight path of the unmanned aerial vehicle according to the time priority contained in the monitoring area and the constraint condition of the data acquisition time of the unmanned aerial vehicle;
and controlling the unmanned aerial vehicle to acquire data according to the optimized flight path of the unmanned aerial vehicle.
In a possible embodiment of the present disclosure, the step of obtaining the priority of the event and the event occurrence area, classifying the event according to the priority, mapping the classification result to the event occurrence area, and determining the monitoring area of the event includes the steps of determining the monitoring area of the event, where the step includes
And determining the region sequence of data acquisition according to the descending order of the priority.
In a possible embodiment of the present disclosure, before the step of determining the location information of the data collection area and the location information of the management node according to the monitoring area, the method further includes the following steps:
determining a value score of the monitoring area according to events contained in the monitoring area;
and determining that the value score is smaller than a preset value score threshold value, and rejecting the monitoring area.
In a possible embodiment of the solution of the present application, the step of determining a value score according to the priority of the events contained in the monitoring area comprises:
acquiring the area of the monitoring area and protection points in the area;
calculating a first sub-value of the monitoring region according to the region area, and calculating a second sub-value of the monitoring region according to the number of protection points in the region;
obtaining the number of protection points in the adjacent area of the monitoring area to determine a third sub-value;
and carrying out weighted summation on the first sub-value, the second sub-value and the third sub-value to obtain the value score.
In a feasible embodiment of the present application, the aggregating the location information of the data collection area and the location information of the management node to determine the constraint condition of the data collection time of the unmanned aerial vehicle includes:
acquiring a first position coordinate of an unmanned aerial vehicle and a second position coordinate of a management node, and determining a first distance between the first position coordinate and the second position coordinate;
determining the flight time of the unmanned aerial vehicle according to the first distance, the communication radius of the management node and the flight speed of the unmanned aerial vehicle;
and accumulating the flight time according to a management node in the monitored area to obtain the total area data acquisition time, and determining the constraint condition according to the total area data acquisition time.
In a possible embodiment of the present disclosure, the step of optimizing the flight path of the drone according to the time priority included in the monitoring area and the constraint condition of the data acquisition time of the drone includes:
summarizing the monitoring area to construct an acquisition area set;
selecting the management node from the collection area set as a node of the unmanned aerial vehicle flight path according to the descending order of the value scores of the monitoring areas; the consumption time of the flight path of the unmanned aerial vehicle is not more than the total time of regional data acquisition in the constraint condition.
In a possible embodiment of the present application, the step of controlling the drone to perform data acquisition according to the optimized flight path of the drone includes:
and controlling the unmanned aerial vehicle to establish communication connection with the management node, and acquiring data through the communication connection.
In a second aspect, the present invention further provides a data acquisition system for multiple event monitoring, including:
the data acquisition module is used for acquiring the priority of an event and an event occurrence area, classifying the event according to the priority, mapping a classification result to the event occurrence area and determining a monitoring area of the event;
the data processing module is used for determining the position information of the data collection area and the position information of the management node according to the monitoring area; converging the position information of the data collection area and the position information of the management node, and determining a constraint condition of data collection time of the unmanned aerial vehicle;
the path generation module is used for optimizing the flight path of the unmanned aerial vehicle according to the time priority contained in the monitoring area and the constraint condition of the data acquisition time of the unmanned aerial vehicle;
and the behavior control module is used for controlling the unmanned aerial vehicle to acquire data according to the optimized flight path of the unmanned aerial vehicle.
In a third aspect, the present invention further provides an unmanned aerial vehicle device for data acquisition of multiple event monitoring, including:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is caused to execute the data collection method for multiple event monitoring of the first aspect.
In a fourth aspect, the present invention also provides a storage medium, in which a processor-executable program is stored, and the processor-executable program is used for executing the method in the first aspect when being executed by a processor.
Advantages and benefits of the present invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention:
according to the technical scheme, the method and the device for monitoring the unmanned aerial vehicle data are used for classifying the type of the monitored area mapping according to the priority of the event, so that the event with higher priority can be monitored or processed preferentially, the priority of the event is used as guidance, the efficiency of unmanned aerial vehicle data acquisition can be effectively improved, and the value density of the data acquired by the unmanned aerial vehicle can be improved; the method integrates the position information of the data collection area, the position information of the management node and the constraint condition of the passing time, improves the efficiency, ensures the real-time performance of data collection, and can flexibly meet the requirement and the target of rapid monitoring of multiple events and multiple areas.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flowchart illustrating steps of a data collection method for multiple event monitoring according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating the steps of screening monitored areas according to an embodiment of the present invention;
FIG. 3 is a flowchart of the steps for calculating a value score for a monitored area in an embodiment of the present invention;
FIG. 4 is a flowchart illustrating steps for determining constraints on data acquisition time for an UAV according to an embodiment of the present invention;
fig. 5 is a flowchart of the steps for optimizing the flight path of the drone in an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
Compared with the traditional Internet of things, the Internet of things applied to emergency monitoring needs to meet the following characteristics: firstly, the event monitoring has emergencies and urgency, so that the data collection of the internet of things for monitoring the emergencies needs to meet certain time limit requirements. Secondly, with the application scale of the novel internet of things, the number of nodes participating in sensing and monitoring and the density increasing and expanding, the whole system is required to sense and acquire data of different events in a plurality of areas, and the monitoring of the data of the internet of things assisted by the current unmanned aerial vehicle brings huge challenges to the sensing and collection of the data of the internet of things assisted by multiple events (namely a plurality of tasks, such as in an urban area, the events of fire, traffic violation, underground water pipe burst and the like can happen at the same time). Finally, in a wide range of internet of things applications, a single event can occur in multiple regions, with different regions representing different values. In conclusion, how to acquire multi-event data under the condition of meeting the time constraint condition becomes one of the hot problems of the current research of the unmanned aerial vehicle-assisted internet of things.
The technical scheme of the application uses the collected data value as a scheme core, improves and optimizes the data collection process of the existing unmanned aerial vehicle Internet of things, and the main processing flow of the unmanned aerial vehicle Internet of things data collection method for multi-event monitoring is as follows: inputting monitoring areas of a plurality of events needing data acquisition; calculating the value of the monitoring area; selecting a candidate data acquisition area; the management nodes in the region gather the related data according to the importance; calculating the data acquisition time of the unmanned aerial vehicle in each area; optimizing the unmanned aerial vehicle path based on the value density; and finishing data acquisition.
In a first aspect, as shown in fig. 1, the data collection method for multiple event monitoring provided in the present application may include steps S100 to S500:
s100, acquiring the priority of an event and an event occurrence area, classifying the event according to the priority, mapping a classification result to the event occurrence area, and determining a monitoring area of the event;
specifically, the priority of an event input by a user of a tiger hill, and the position, the area and other parameters of an area to be monitored or an event occurrence area corresponding to the event are first implemented. In the embodiment, a data processing center, such as a server, may classify a monitoring area according to parameters such as event priority and monitoring area; mapping and matching the monitoring area according to the event priority; so as to form a set of regions formed by different events, and determine the sequence of each region in the data acquisition process according to the priority.
In some alternative embodiments, the step S100 of obtaining the priority of the event and the event occurrence region, classifying the event according to the priority, mapping the classification result to the event occurrence region, and determining the monitoring region of the event may further include the sub-step S110:
s110, determining the region sequence of data acquisition according to descending order of priority;
in particular, in one area
In the region to be monitored, a ═ is1，a2，...，anFor any a E to A, the method has parameter information such as parameter position, area, event type and the like; and classifying and matching and mapping the A according to the event priority. Illustratively, in a slice zone
There are usually 3 events to be monitored, the monitoring area is (a, b, c, d, e, f, g, h); the corresponding event is (1, 2, 3, 1, 2, 1, 1, 1), wherein the smaller the number of events, the higher the priority; further, event 1 monitoring area (a, d, f, g, h), event 2 monitoring area (b, e), and event 3 monitoring area (c) can be classified according to event priority. The embodiment further arranges the detection regions in descending order according to the prioritySequencing, and preferentially acquiring the monitoring areas (a, d, f, g and h) due to the highest priority of the event 1; the order of data acquisition is therefore: the region (a, d, f, g, h) corresponding to event 1 is collected first, then the region (b, e) corresponding to event 2, and finally the region (c) corresponding to event 3. Therefore, the first optimization of the data acquisition sequence of the monitoring area is completed based on the event priority; subsequent embodiments may employ the zone values for secondary optimization.
Before the embodiment determines the data collection area location information and the management node location information, i.e. before step S200, step S120 and step S130 may be further included:
s120, determining a value score of the monitoring area according to events contained in the monitoring area;
specifically, in an embodiment, the server of the system performs a value assessment for each monitored area based on the event type. For example, the value score of (a, d, f, g, h) is (50, 60, 40, 10, 35).
S130, determining that the value score is smaller than a preset value score threshold value, and rejecting the monitoring area.
Specifically, screening the monitoring area according to the value score of the monitoring area; i.e., when the value is less than a preset value score threshold epsilon, the region is deleted from the data collection area. As shown in fig. 2, value scores of all monitoring areas are searched, and then value scores corresponding to the monitoring areas a e.g. A are retrieved; judging whether the value corresponding to the monitoring area a is greater than a threshold value epsilon or not; if so, add a to the candidate data collection set B. And repeating the processes of retrieval and addition until the judgment of the values of all the monitoring areas is completed, completing the candidate data acquisition set B, and outputting the candidate data acquisition set. Illustratively, if ε is 30 in the example, the data collection area after (a, d, f, g, h) screening is (a, d, f, h).
S200, determining position information of a data collection area and position information of a management node according to a monitoring area;
specifically, according to the classification result of the monitoring region obtained after the first optimization, the embodiment collects information such as the data collection region position and the position of a management node (for example, an internet of things monitoring node) of each detection region, determines a set of participating sensing nodes, and performs data aggregation through the management node in the region.
S300, gathering position information of a data collection area and position information of a management node, and determining a constraint condition of data collection time of the unmanned aerial vehicle;
specifically, according to the position information of the data collection area and the position information of the management node acquired in step S200, the size of the converged data volume of the management node, the communication radius, the communication rate with the unmanned aerial vehicle, and the like are further determined to determine the residence time of the unmanned aerial vehicle and the time consumption for collecting the data of the unmanned aerial vehicle in the area; and the time consumption of unmanned aerial vehicle data acquisition is used as the constraint condition of the man-machine data acquisition time.
S400, optimizing the flight path of the unmanned aerial vehicle according to the time priority contained in the monitoring area and the constraint condition of the data acquisition time of the unmanned aerial vehicle;
specifically, on the basis that the regional value scores are determined in steps S120-S130, the embodiment may optimize the flight path of the unmanned aerial vehicle by using a greedy algorithm according to the regional value scores and the unmanned aerial vehicle data acquisition time constraint conditions. Illustratively, the time constraint is 30 minutes, the value score of (a, d, f, g, h) is (50, 60, 40, 10, 35), which is determined to be (a, d, f, h) by predictive screening, and the process of using the greedy algorithm is as follows. Firstly, adding an unmanned aerial vehicle flight path F (O, d) (wherein O is an unmanned aerial vehicle origin) into an area corresponding to the maximum value score 60, and calculating the time for completing data acquisition of the unmanned aerial vehicle O-d to be 10min and less than 30min so as to meet a time constraint condition; selecting an area a with the maximum value except d, adding a flight path F ═ O, d, a }, and calculating to obtain that the time for completing data acquisition of the unmanned aerial vehicle O-d-a is 23min and still less than 30min, so that the time constraint condition is met; repeating the steps, updating a path F to be { O, d, a, F }, and calculating the time of completing data acquisition of the unmanned aerial vehicle O-d-a-F to be 27min which is less than 30 min; the time constraint condition is satisfied. And repeating the steps, updating F to be { O, d, a, F, h }, calculating the time for completing data acquisition of the unmanned aerial vehicle O-d-a-F-h to be 37min, wherein the time is more than 30min, and if the time constraint condition is not met, deleting h from F, and outputting F to be { O, d, a, F }.
S500, controlling the unmanned aerial vehicle to acquire data according to the optimized flight path of the unmanned aerial vehicle;
specifically, the unmanned aerial vehicle flies to the top of each area management node according to the optimized flight trajectory. In some optional embodiments, in the process of controlling the unmanned aerial vehicle to perform data acquisition, the unmanned aerial vehicle may also be controlled to establish a communication connection with the management node, and perform data acquisition through the communication connection; the unmanned aerial vehicle establishes communication connection with the management node, and data acquisition is carried out after wireless connection is established.
In some alternative embodiments, the step of determining a value score based on the priority of the events contained in the monitoring area S120 may comprise the steps of S121-S124:
s121, acquiring the area of a monitoring area and protection points in the area;
specifically, as shown in FIG. 3, the embodiment first obtains the relevant parameters of all the monitoring areas a ∈ A inputted, including but not limited to the area Sa(ii) a Protection points or value points in a region, such as an urban district, a building or a water source point in a district; the number of adjacent protection points of the area. In the embodiment, the calculation rule of the region proximity may adopt a rule that the region boundary extends outward for a certain distance, for example, 100 m; the extent of the outward extension may be determined based on the dynamically changing nature of the region mapping event.
S122, calculating a first sub-value of the monitoring region according to the region area, and calculating a second sub-value of the monitoring region according to the number of protection points in the region;
specifically, as shown in fig. 3, the embodiment calculates a sub-value corresponding to the area of the monitoring region by the area of the region. Wherein the area of the whole sheet region is
The sub-value corresponding to the area of the monitoring area is VS(a) And satisfy
According to the embodiment, the sub-value corresponding to the number of the protection points in the monitoring area is calculated according to the number of the protection points in the area. The specific calculation process is as follows, the total number of the protection points in the monitoring area of the whole area is recorded as
The number of protection points in the region is NaIf so, the sub-value V corresponding to the protection point in the monitoring areaP(a) And satisfy
S123, obtaining the number of protection points in the adjacent area of the monitoring area to determine a third sub-value;
specifically, as shown in fig. 3, in the embodiment, the sub-value corresponding to the number of the protection points adjacent to the monitoring area is calculated according to the number of the protection points adjacent to the monitoring area. The specific calculation process is as follows, and the total number of the protection points outside the monitoring area of the whole area is recorded as
The number of the adjacent protection points in the area is MaThen the sub-value V corresponding to the protection point is adjacent to the monitoring areaE(a) And satisfy
S124, carrying out weighted summation on the first sub-value, the second sub-value and the third sub-value to obtain a value score;
in particular, embodiments calculate the total value y (a) of the monitored area, and V (a) ═ α VS(a)+βVP(a)+γVE(a) In that respect Wherein α + β + γ is 1, and in the examples α is 0.1, β is 0.4, and γ is 0.5; and the values of alpha, beta and gamma can be dynamically adjusted according to the actual application characteristics in different implementation environments.
In some optional embodiments, the step S300 of aggregating the data collection area location information and the management node location information, and determining the constraint condition of the data collection time of the drone may include steps S310 to S330:
s310, acquiring a first position coordinate of the unmanned aerial vehicle and a second position coordinate of the management node, and determining a first distance between the first position coordinate and the second position coordinate;
specifically, as shown in fig. 4, the current drone position coordinates (X) are inputu，Yu) And the position coordinates (X) of the management node g of the next data acquisition area ag，Yg) Calculating the distance d between the unmanned aerial vehicle and the management node,
s320, determining the flight time of the unmanned aerial vehicle according to the first distance, the communication radius of the management node and the flight speed of the unmanned aerial vehicle:
specifically, calculating the flight time t of the unmanned aerial vehicle in a non-data communication statef. Noting the flight speed of the unmanned aerial vehicle as vfWhen the communication radius of the management node is R, t isf＝(d-R)/vf。
S330, accumulating the flight time according to the management node in the monitoring area to obtain the total area data acquisition time, and determining a constraint condition according to the total area data acquisition time;
specifically, the unmanned aerial vehicle stays in the area for the time t required for communicationc. Recording the quantity D of the regional management nodes needing to be uploaded and the communication rate vcThe required time t for uploading the data of the areac＝D/vc. Outputting the total time T (a) t of the unmanned aerial vehicle for collecting the area dataf+tc。
In some optional embodiments, the step S400 of optimizing the flight path of the drone according to the time priority included in the monitoring area and the constraint condition of the data acquisition time of the drone may include steps S410 to S420:
s410, summarizing the monitoring areas to construct an acquisition area set;
in particular, the embodiment inputs a set B of candidate data collection areas, and corresponding prices of management nodes and areasValue score VB。
S420, selecting management nodes from the collection area set as nodes of the flight path of the unmanned aerial vehicle according to descending order of the value scores of the monitoring areas;
and the consumption time of the flight path of the unmanned aerial vehicle is not more than the total time of regional data acquisition in the constraint condition. Specifically, as shown in fig. 5, in the embodiment, a management node g (ymax) corresponding to an area with the largest area value is selected from B as a next node in the flight path of the unmanned aerial vehicle, that is, F ═ { O, g (vmax) }, and bg corresponding to g (vmax) is deleted from B. And then, calculating the total time T required by the unmanned aerial vehicle to finish data acquisition according to the F path. And judging whether the time satisfies T < TlimitRequirement (T)ltmitAnd a data acquisition constraint condition), if the data acquisition constraint condition is not met, deleting the newly added node from the F, and directly outputting the flight path of the unmanned aerial vehicle. If T < T is satisfiedlimitRepeating the selection and addition process of the nodes until the B is an empty set.
In a second aspect, the technical solution of the present application further provides a data acquisition system for multiple event monitoring, which includes:
the data acquisition module is used for acquiring the priority of the event and the event occurrence area, classifying the event according to the priority, mapping the classification result to the event occurrence area and determining the monitoring area of the event;
the data processing module is used for determining the position information of the data collection area and the position information of the management node according to the monitoring area; collecting position information of the data collection area and position information of the management node, and determining a constraint condition of data collection time of the unmanned aerial vehicle;
the path generation module is used for optimizing the flight path of the unmanned aerial vehicle according to the time priority contained in the monitoring area and the constraint condition of the data acquisition time of the unmanned aerial vehicle;
and the behavior control module is used for controlling the unmanned aerial vehicle to acquire data according to the optimized flight path of the unmanned aerial vehicle.
In a third aspect, the technical solution of the present application further provides a data-collecting unmanned aerial vehicle device for multiple event monitoring, which includes at least one processor; at least one memory for storing at least one program; when the at least one program is executed by the at least one processor, the at least one processor is caused to perform the data acquisition method of multiple event monitoring as in the first aspect.
Specifically, the unmanned aerial vehicle is provided with the wireless communication module in the embodiment, and can establish wireless communication connection with the ground internet of things sensing node to complete collection of related sensing data. The ground internet of things adopts a cluster network structure to construct the whole system, namely one or more management nodes and a plurality of common nodes (sensing nodes) are arranged in one cluster. The management node converges the data information sensed by the common node according to a certain rule, and then sends converged data to the unmanned aerial vehicle. And finally, the unmanned aerial vehicle transmits the data to a remote server to finish the rapid and efficient collection of the data. Adopt unmanned aerial vehicle to gather thing networking perception data, can realize the quick monitoring to emergency, break through traditional network in data acquisition on the topography ground, need be equipped with the restriction that basic network equipped (for example ripe 4G communication base station).
An embodiment of the present invention further provides a storage medium storing a program, where the program is executed by a processor to implement the method in the first aspect.
From the above specific implementation process, it can be concluded that the technical solution provided by the present invention has the following advantages or advantages compared to the prior art:
according to the technical scheme, the method classifies the type of the affair mapped by the monitoring area according to the priority of the event; the method can enable the high-priority event to be preferentially subjected to unmanned aerial vehicle data acquisition. Then, calculating the regional value according to the relevant parameters of the monitored region; primary screening is carried out on the monitored area according to the area value, and the unmanned aerial vehicle acquisition value density low area data can be deleted in the step; the value of data acquisition is improved. Meanwhile, an area value greedy algorithm is adopted to optimize the unmanned aerial vehicle path; this step promotes unmanned aerial vehicle data collection's value density. The multi-event unmanned aerial vehicle internet of things data acquisition method has the advantages that the conditions are met earlier, the data value density is improved, the efficiency is higher, the method is more flexible, and the requirements and the targets of rapid monitoring of multiple events and multiple regions are met.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and/or features may be integrated in a single physical device and/or software module, or one or more of the functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. The data acquisition method for multi-event monitoring is characterized by comprising the following steps of:
acquiring the priority of an event and an event occurrence area, classifying the event according to the priority, mapping a classification result to the event occurrence area, and determining a monitoring area of the event;
determining data collection area position information and management node position information according to the monitoring area;
converging the position information of the data collection area and the position information of the management node, and determining a constraint condition of data collection time of the unmanned aerial vehicle;
optimizing the flight path of the unmanned aerial vehicle according to the time priority contained in the monitoring area and the constraint condition of the data acquisition time of the unmanned aerial vehicle;
and controlling the unmanned aerial vehicle to acquire data according to the optimized flight path of the unmanned aerial vehicle.
2. The method for collecting data of multi-event monitoring as claimed in claim 1, wherein the step of obtaining the priority of the event and the event occurrence area, classifying the event according to the priority, mapping the classification result to the event occurrence area, and determining the monitoring area of the event comprises
And determining the region sequence of data acquisition according to the descending order of the priority.
3. The method of data collection for multiple event monitoring of claim 1, wherein said method further comprises the step of, prior to said step of determining data collection area location information and management node location information based on said monitored area:
determining a value score of the monitoring area according to events contained in the monitoring area;
and determining that the value score is smaller than a preset value score threshold value, and rejecting the monitoring area.
4. The method of claim 3, wherein the step of determining a value score based on the priority of the events contained in the monitoring area comprises:
acquiring the area of the monitoring area and protection points in the area;
calculating a first sub-value of the monitoring region according to the region area, and calculating a second sub-value of the monitoring region according to the number of protection points in the region;
obtaining the number of protection points in the adjacent area of the monitoring area to determine a third sub-value;
and carrying out weighted summation on the first sub-value, the second sub-value and the third sub-value to obtain the value score.
5. The method of claim 1, wherein the step of aggregating the data collection area location information and the management node location information to determine the constraints of the drone data collection time comprises:
acquiring a first position coordinate of an unmanned aerial vehicle and a second position coordinate of a management node, and determining a first distance between the first position coordinate and the second position coordinate;
determining the flight time of the unmanned aerial vehicle according to the first distance, the communication radius of the management node and the flight speed of the unmanned aerial vehicle;
and accumulating the flight time according to a management node in the monitored area to obtain the total area data acquisition time, and determining the constraint condition according to the total area data acquisition time.
6. The method of claim 5, wherein the step of optimizing the flight path of the drone according to the time priority contained in the monitored area and the constraints on the time of data acquisition of the drone comprises:
summarizing the monitoring area to construct an acquisition area set;
selecting the management node from the collection area set as a node of the unmanned aerial vehicle flight path according to the descending order of the value scores of the monitoring areas; the consumption time of the flight path of the unmanned aerial vehicle is not more than the total time of regional data acquisition in the constraint condition.
7. The method for data collection of multiple event monitoring according to any one of claims 1-6, wherein the step of controlling the drone to collect data according to the optimized drone flight path comprises:
and controlling the unmanned aerial vehicle to establish communication connection with the management node, and acquiring data through the communication connection.
8. A data acquisition system for multiple event monitoring, comprising:
the data acquisition module is used for acquiring the priority of an event and an event occurrence area, classifying the event according to the priority, mapping a classification result to the event occurrence area and determining a monitoring area of the event;
the data processing module is used for determining the position information of the data collection area and the position information of the management node according to the monitoring area; converging the position information of the data collection area and the position information of the management node, and determining a constraint condition of data collection time of the unmanned aerial vehicle;
the path generation module is used for optimizing the flight path of the unmanned aerial vehicle according to the time priority contained in the monitoring area and the constraint condition of the data acquisition time of the unmanned aerial vehicle;
and the behavior control module is used for controlling the unmanned aerial vehicle to acquire data according to the optimized flight path of the unmanned aerial vehicle.
9. Unmanned aerial vehicle equipment of data acquisition of many events monitoring, its characterized in that includes:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, the at least one program causes the at least one processor to execute the multiple event monitored data acquisition method of any of claims 1-7.
10. A storage medium having stored therein a processor-executable program, wherein the processor-executable program, when executed by a processor, is configured to execute the multiple event monitored data acquisition method of any one of claims 1-7.
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