CN113608548A - Unmanned aerial vehicle emergency processing method and system, storage medium and electronic equipment - Google Patents
Unmanned aerial vehicle emergency processing method and system, storage medium and electronic equipment Download PDFInfo
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Abstract
本发明涉及无人机领域，尤其涉及一种无人机应急处理方法、系统、存储介质及电子设备。该方法包括：步骤1，获取受灾地区的基本信息；步骤2，对所述基本信息进行处理，得到无人机派出数量；步骤3，通过多个无人机中的采集设备，对所述受灾地区进行遥感影像数据的采集以及地质数据的采集；步骤4，根据所述遥感影像数据的采集结果以及所述地质数据的采集结果对救援线路进行规划。通过该方法可以达到保证了采集过程中不会出现伤亡，另一方面通过高空采集数据可以更加全面的获取受灾地貌情况，根据采集结果规划救援路线可以有效的保证救援人员及时赶到救援现场，减少受灾人员伤亡，同时保证救援人员的安全的效果。
The present invention relates to the field of unmanned aerial vehicles, in particular to a method, system, storage medium and electronic equipment for emergency treatment of unmanned aerial vehicles. The method includes: step 1, obtaining basic information of the disaster-stricken area; step 2, processing the basic information to obtain the number of drones dispatched; The collection of remote sensing image data and the collection of geological data are carried out in the region; step 4, the rescue route is planned according to the collection result of the remote sensing image data and the collection result of the geological data. This method can ensure that there will be no casualties during the collection process. On the other hand, the disaster-affected landforms can be obtained more comprehensively through high-altitude data collection. Planning rescue routes according to the collection results can effectively ensure that rescuers arrive at the rescue site in time, reducing disaster-affected casualties, while ensuring the safety of rescuers.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle emergency processing method, an unmanned aerial vehicle emergency processing system, a storage medium and electronic equipment.
Background
The damage caused by natural disasters such as forest fires, debris flows and the like is immeasurable, and the damage is more troublesome if buildings and the like are contained in a disaster area. Therefore, it is important for rescue of people in disaster and reconstruction after disaster. In the prior art, the technology for generating the path for rescuing the people in distress does not combine with the landform characteristics, so that the path planning cannot be accurate, in addition, the characteristic that the unmanned aerial vehicle can obtain data without being damaged in the air is not fully utilized, the landform or the disaster area is not safe to be counted manually, meanwhile, the manual counting is not comprehensive and not accurate, if the historical data is analyzed, the characteristic of disaster instantaneous change cannot be processed, so that the rescuers cannot arrive at the rescue site in time, and serious casualties and material damage are caused.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle emergency processing method, an unmanned aerial vehicle emergency processing system, a storage medium and electronic equipment.
The technical scheme for solving the technical problems is as follows: an unmanned aerial vehicle emergency processing method comprises the following steps:
step 1, acquiring basic information of a disaster area;
step 2, processing the basic information to obtain the dispatching quantity of the unmanned aerial vehicles;
step 3, acquiring remote sensing image data and geological data of the disaster area through acquisition equipment in a plurality of unmanned aerial vehicles;
and 4, planning a rescue route according to the acquisition result of the remote sensing image data and the acquisition result of the geological data.
The invention has the beneficial effects that: the technical effect of acquiring the data in the largest range by calling the fewest unmanned aerial vehicles can be achieved by processing the basic information and calculating the number of the unmanned aerial vehicles, so that resources are saved, and the slow rescue speed caused by repeated data acquisition, repeated data calculation and the like is avoided; in addition, the data of the disaster area is collected through the unmanned aerial vehicle, based on the characteristics of the unmanned aerial vehicle, on one hand, casualties can not appear in the data collection process, on the other hand, the overall situation of the disaster area can be comprehensively obtained through high-altitude data collection, and compared with means such as manual investigation report or historical landform data, the efficiency and the accuracy can be greatly improved through the unmanned aerial vehicle, the rescue route can be more effectively planned according to the overall landform collection result, rescue workers can timely arrive at the rescue site, casualties of the disaster people are reduced, and meanwhile, the safety of the rescue workers is guaranteed.
On the basis of the technical scheme, the invention can be further improved as follows.
Further, the basic information includes: a disaster location, a disaster type, and a disaster range.
Further, step 2 specifically comprises:
and processing the disaster range through a multi-factor maximum coverage model to obtain the dispatching quantity of the unmanned aerial vehicles and the designated position of each unmanned aerial vehicle.
The beneficial effect of adopting above-mentioned further scheme is that, can accomplish through the biggest coverage model of multifactor and obtain the data in the biggest range through minimum unmanned aerial vehicle, both saved the resource and can avoid unmanned aerial vehicle to gather the scope repeatedly again, obtain too much repetition data and then influence data calculation, reduce the efficiency scheduling problem that the route generated. Support is provided for rapidly arriving at disaster areas to help the disaster victims.
Further, step 4 specifically comprises:
the method comprises the steps of inputting collected environment data of different areas into a prediction model to obtain road sections with risks in the areas, integrating the road sections with risks in all the areas, generating an optimal path according to integrated branching road sections, sending the optimal path to a rescue command center, and calling rescue goods and materials by the rescue command center according to the optimal path, wherein the environment data comprise collection results of remote sensing image data and collection results of geological data.
The method has the advantages that the risk road section in the disaster area is obtained through the prediction model, the optimal path is generated according to the risk road section, on one hand, the path can be ensured to be safe, the safety of the environment where rescue workers are located is high, on the other hand, the probability of temporary route blocking during the rescue process is low because the optimal path avoids the risk road section, and the rescue workers can quickly reach the disaster site for rescue.
Further, step 4 is followed by:
and 5, acquiring remote sensing image data and geological data of the disaster area again at preset time intervals, and repeating the step 4 until rescue is finished.
The beneficial effect of adopting the above further scheme is that data are repeatedly acquired at preset time intervals, the optimal path is continuously monitored, and if an emergency happens to cause path blockage or is suddenly uncontrollable due to a disaster, the rescue task can be completed while casualties are reduced through adjustment of the optimal path.
Another technical solution of the present invention for solving the above technical problems is as follows: an unmanned aerial vehicle emergency processing system, comprising:
the acquisition module is used for acquiring basic information of a disaster area;
the processing module is used for processing the basic information to obtain the dispatching quantity of the unmanned aerial vehicles;
the acquisition module is used for acquiring remote sensing image data and geological data of the disaster area through a plurality of unmanned aerial vehicles;
and the planning module is used for planning a rescue line according to the acquisition result of the remote sensing image data and the acquisition result of the geological data.
The invention has the beneficial effects that: the technical effect of acquiring the data in the largest range by calling the fewest unmanned aerial vehicles can be achieved by processing the basic information and calculating the number of the unmanned aerial vehicles, so that resources are saved, and the slow rescue speed caused by repeated data acquisition, repeated data calculation and the like is avoided; in addition, the data of the disaster area is collected through the unmanned aerial vehicle, based on the characteristics of the unmanned aerial vehicle, on one hand, casualties can not appear in the data collection process, on the other hand, the overall situation of the disaster area can be comprehensively obtained through high-altitude data collection, and compared with means such as manual investigation report or historical landform data, the efficiency and the accuracy can be greatly improved through the unmanned aerial vehicle, the rescue route can be more effectively planned according to the overall landform collection result, rescue workers can timely arrive at the rescue site, casualties of the disaster people are reduced, and meanwhile, the safety of the rescue workers is guaranteed.
Further, the basic information includes: a disaster location, a disaster type, and a disaster range.
Further, the processing module is specifically configured to:
and processing the disaster range through a multi-factor maximum coverage model to obtain the dispatching quantity of the unmanned aerial vehicles and the designated position of each unmanned aerial vehicle.
The beneficial effect of adopting above-mentioned further scheme is that, can accomplish through the biggest coverage model of multifactor and obtain the data in the biggest range through minimum unmanned aerial vehicle, both saved the resource and can avoid unmanned aerial vehicle to gather the scope repeatedly again, obtain too much repetition data and then influence data calculation, reduce the efficiency scheduling problem that the route generated. Support is provided for rapidly arriving at disaster areas to help the disaster victims.
Further, the planning module is specifically configured to:
the method comprises the steps of inputting collected environment data of different areas into a prediction model to obtain road sections with risks in the areas, integrating the road sections with risks in all the areas, generating an optimal path according to integrated branching road sections, sending the optimal path to a rescue command center, and calling rescue goods and materials by the rescue command center according to the optimal path, wherein the environment data comprise collection results of remote sensing image data and collection results of geological data.
The method has the advantages that the risk road section in the disaster area is obtained through the prediction model, the optimal path is generated according to the risk road section, on one hand, the path can be ensured to be safe, the safety of the environment where rescue workers are located is high, on the other hand, the probability of temporary route blocking during the rescue process is low because the optimal path avoids the risk road section, and the rescue workers can quickly reach the disaster site for rescue.
Further, still include: and the repeating module is used for collecting the remote sensing image data and the geological data of the disaster area again at preset time intervals, and repeating the task of the planning module until the rescue is finished.
The beneficial effect of adopting the above further scheme is that data are repeatedly acquired at preset time intervals, the optimal path is continuously monitored, and if an emergency happens to cause path blockage or is suddenly uncontrollable due to a disaster, the rescue task can be completed while casualties are reduced through adjustment of the optimal path.
Another technical solution of the present invention for solving the above technical problems is as follows: a storage medium having instructions stored therein, which when read by a computer, cause the computer to perform a method of emergency handling for a drone as claimed in any one of the preceding claims.
The invention has the beneficial effects that: the technical effect of acquiring the data in the largest range by calling the fewest unmanned aerial vehicles can be achieved by processing the basic information and calculating the number of the unmanned aerial vehicles, so that resources are saved, and the slow rescue speed caused by repeated data acquisition, repeated data calculation and the like is avoided; in addition, the data of the disaster area is collected through the unmanned aerial vehicle, based on the characteristics of the unmanned aerial vehicle, on one hand, casualties can not appear in the data collection process, on the other hand, the overall situation of the disaster area can be comprehensively obtained through high-altitude data collection, and compared with means such as manual investigation report or historical landform data, the efficiency and the accuracy can be greatly improved through the unmanned aerial vehicle, the rescue route can be more effectively planned according to the overall landform collection result, rescue workers can timely arrive at the rescue site, casualties of the disaster people are reduced, and meanwhile, the safety of the rescue workers is guaranteed.
Another technical solution of the present invention for solving the above technical problems is as follows: an electronic device comprising a memory, a processor and a program stored on the memory and running on the processor, wherein the processor executes the program to implement a method for emergency handling of a drone according to any one of the above.
The invention has the beneficial effects that: the technical effect of acquiring the data in the largest range by calling the fewest unmanned aerial vehicles can be achieved by processing the basic information and calculating the number of the unmanned aerial vehicles, so that resources are saved, and the slow rescue speed caused by repeated data acquisition, repeated data calculation and the like is avoided; in addition, the data of the disaster area is collected through the unmanned aerial vehicle, based on the characteristics of the unmanned aerial vehicle, on one hand, casualties can not appear in the data collection process, on the other hand, the overall situation of the disaster area can be comprehensively obtained through high-altitude data collection, and compared with means such as manual investigation report or historical landform data, the efficiency and the accuracy can be greatly improved through the unmanned aerial vehicle, the rescue route can be more effectively planned according to the overall landform collection result, rescue workers can timely arrive at the rescue site, casualties of the disaster people are reduced, and meanwhile, the safety of the rescue workers is guaranteed.
Drawings
Fig. 1 is a schematic flow chart of an emergency processing method for an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 2 is a structural framework diagram provided in an embodiment of an emergency processing system for an unmanned aerial vehicle according to the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with examples which are set forth to illustrate, but are not to be construed to limit the scope of the invention.
As shown in fig. 1, an emergency handling method for an unmanned aerial vehicle includes:
step 1, acquiring basic information of a disaster area;
step 2, processing the basic information to obtain the dispatching quantity of the unmanned aerial vehicles;
step 3, acquiring remote sensing image data and geological data of the disaster area through acquisition equipment in a plurality of unmanned aerial vehicles;
and 4, planning a rescue route according to the acquisition result of the remote sensing image data and the acquisition result of the geological data.
In some possible embodiments, the technical effect of acquiring the data in the largest range by calling the fewest unmanned aerial vehicles can be achieved by processing the basic information and calculating the number of the unmanned aerial vehicles, so that resources are saved, and meanwhile, the slow rescue speed caused by repeated data acquisition, repeated data calculation and the like is avoided; in addition, the data of the disaster area is collected through the unmanned aerial vehicle, based on the characteristics of the unmanned aerial vehicle, on one hand, casualties can not appear in the data collection process, on the other hand, the overall situation of the disaster area can be comprehensively obtained through high-altitude data collection, and compared with means such as manual investigation report or historical landform data, the efficiency and the accuracy can be greatly improved through the unmanned aerial vehicle, the rescue route can be more effectively planned according to the overall landform collection result, rescue workers can timely arrive at the rescue site, casualties of the disaster people are reduced, and meanwhile, the safety of the rescue workers is guaranteed.
It should be noted that the basic information of the disaster area includes, but is not limited to, the disaster location, the disaster type, and the disaster range; calculating the disaster area in the basic information through a multi-factor maximum coverage model or a coverage model built by the unmanned aerial vehicle, obtaining the number of unmanned aerial vehicles needing to be dispatched, acquiring data of a disaster area through acquisition equipment installed on the unmanned aerial vehicle, inputting an acquisition result into a prediction model, obtaining a risk road section, and planning an optimal path based on the risk road section, wherein the optimal path needs to ensure that casualties are the lowest and a path which is the fastest to a specified rescue area is reached.
In embodiment 1, after receiving the request support information, the rescue command center calls a processor to obtain basic information including a disaster area, a disaster type and a disaster area, plans the disaster area through a multi-factor maximum coverage model to obtain the number of unmanned aerial vehicles to be dispatched, and controls the unmanned aerial vehicles to reach respective designated positions, and then the unmanned aerial vehicles acquire data including remote sensing image data, geological data and the like from the disaster area through acquisition devices carried by the unmanned aerial vehicles. And inputting the acquired data into a pre-established prediction model to obtain road section data information with risks in the disaster area, and integrating the road section data information with the risks to obtain a safe and shortest-time rescue path avoiding the risk road section.
Preferably, in any of the above embodiments, the basic information includes: a disaster location, a disaster type, and a disaster range.
The basic information includes not only the disaster site, the disaster type, and the disaster range, but also information such as the number of persons in a disaster and the presence or absence of a building.
Preferably, in any of the above embodiments, step 2 is specifically:
and processing the disaster range through a multi-factor maximum coverage model to obtain the dispatching quantity of the unmanned aerial vehicles and the designated position of each unmanned aerial vehicle.
It should be noted that the calculation may be performed through a multi-factor maximum coverage model, and the disaster-affected range may also be processed through a preset coverage model.
In some possible implementation modes, the data in the maximum range can be obtained through the fewest unmanned aerial vehicles through the multi-factor maximum coverage model, so that the resources are saved, the repeated acquisition range of the unmanned aerial vehicles can be avoided, the data calculation is influenced by obtaining excessive repeated data, and the route generation efficiency is reduced. Support is provided for rapidly arriving at disaster areas to help the disaster victims.
Preferably, in any of the above embodiments, step 4 is specifically:
the method comprises the steps of inputting collected environment data of different areas into a prediction model to obtain road sections with risks in the areas, integrating the road sections with risks in all the areas, generating an optimal path according to integrated branching road sections, sending the optimal path to a rescue command center, and calling rescue goods and materials by the rescue command center according to the optimal path, wherein the environment data comprise collection results of remote sensing image data and collection results of geological data.
In some possible implementation manners, the risk road section in the disaster area is obtained through the prediction model, on one hand, the optimal path is generated according to the risk road section, so that the path is relatively safe, the safety of the environment where rescue workers are located is relatively high, on the other hand, the optimal path avoids the risk road section, so that the possibility of temporarily blocking the path in the rescue process is relatively low, and the rescue workers can quickly reach the disaster site for rescue.
In embodiment 2, after receiving the request support information, the rescue command center calls a processor to obtain basic information including a disaster area, a disaster type, and a disaster area, plans the disaster area through a multi-factor maximum coverage model to obtain the number of unmanned aerial vehicles to be dispatched, and controls the unmanned aerial vehicles to reach respective designated positions, and then the unmanned aerial vehicles acquire data including remote sensing image data, geological data and the like from the disaster area through acquisition devices carried by the unmanned aerial vehicles. The method comprises the steps of inputting collected data into a pre-established prediction model to obtain road data information with risks in a disaster area, integrating the road data information with risks, inputting the integrated road data information into a pre-established optimal path generation model to obtain an optimal path, sending the optimal path to a rescue command center, judging the quantity of goods and materials and the quantity of rescuers needed by rescue according to remote sensing data fed back by an unmanned aerial vehicle by the rescue command center, and distributing the goods and the personnel according to different paths.
Preferably, in any of the above embodiments, step 4 is further followed by:
and 5, acquiring remote sensing image data and geological data of the disaster area again at preset time intervals, and repeating the step 4 until rescue is finished.
In some possible implementation modes, data are repeatedly acquired at preset time intervals, the optimal path is continuously monitored, and if an emergency happens to cause path blockage or the emergency is suddenly uncontrollable due to a disaster, the rescue task can be completed while casualties are reduced through adjustment of the optimal path.
In embodiment 3, after receiving the request support information, the rescue command center calls a processor to obtain basic information including a disaster area, a disaster type, and a disaster area, plans the disaster area through a multi-factor maximum coverage model to obtain the number of unmanned aerial vehicles to be dispatched, and controls the unmanned aerial vehicles to reach respective designated positions, and then the unmanned aerial vehicles acquire data including remote sensing image data and geological data from the disaster area through acquisition devices carried by the unmanned aerial vehicles. The method comprises the steps of inputting collected data into a pre-established prediction model to obtain road data information with risks in a disaster area, integrating the road data information with risks, inputting the integrated road data information into a pre-established optimal path generation model to obtain a first optimal path, sending the first optimal path to a rescue command center, judging the quantity of goods and materials and the quantity of rescue personnel required by rescue according to remote sensing data fed back by an unmanned aerial vehicle by the rescue command center, and distributing the goods and the personnel according to different paths. The rescue command center keeps contact with each rescue squad in real time, the processor plans the optimal path again at preset time intervals, if the optimal path is consistent with the first optimal path, the rescue personnel carries out rescue according to the first optimal path, if the first optimal path is different from the first path, the rescue command center needs to transmit the path change information to the rescue squad, the rescue squad carries out rescue according to the changed path, and similarly, after the rescue command center reaches the rescue place, when the disaster-stricken personnel are expanded and rescued, the withdrawal path is planned again, the generation mode of the optimal path is the same as that of the optimal path, the rescue squadrons withdraw according to the withdrawal path transmitted by the rescue command center, and simultaneously, in the evacuation process, the route is still re-planned at preset time intervals, so that the personal safety of rescue workers is guaranteed, and the unmanned aerial vehicle is recalled by the rescue command center until the rescue is finished.
As shown in fig. 2, an emergency treatment system for unmanned aerial vehicle includes:
an obtaining module 100, configured to obtain basic information of a disaster area;
the processing module 200 is configured to process the basic information to obtain the number of the distributed unmanned aerial vehicles;
the acquisition module 300 is used for acquiring remote sensing image data and geological data of the disaster area through a plurality of unmanned aerial vehicles;
and the planning module 400 is used for planning a rescue route according to the acquisition result of the remote sensing image data and the acquisition result of the geological data.
In some possible embodiments, the technical effect of acquiring the data in the largest range by calling the fewest unmanned aerial vehicles can be achieved by processing the basic information and calculating the number of the unmanned aerial vehicles, so that resources are saved, and meanwhile, the slow rescue speed caused by repeated data acquisition, repeated data calculation and the like is avoided; in addition, the data of the disaster area is collected through the unmanned aerial vehicle, based on the characteristics of the unmanned aerial vehicle, on one hand, casualties can not appear in the data collection process, on the other hand, the overall situation of the disaster area can be comprehensively obtained through high-altitude data collection, and compared with means such as manual investigation report or historical landform data, the efficiency and the accuracy can be greatly improved through the unmanned aerial vehicle, the rescue route can be more effectively planned according to the overall landform collection result, rescue workers can timely arrive at the rescue site, casualties of the disaster people are reduced, and meanwhile, the safety of the rescue workers is guaranteed.
Preferably, in any of the above embodiments, the basic information includes: a disaster location, a disaster type, and a disaster range.
Preferably, in any of the above embodiments, the processing module 200 is specifically configured to:
and processing the disaster range through a multi-factor maximum coverage model to obtain the dispatching quantity of the unmanned aerial vehicles and the designated position of each unmanned aerial vehicle.
In some possible implementation modes, the data in the maximum range can be obtained through the fewest unmanned aerial vehicles through the multi-factor maximum coverage model, so that the resources are saved, the repeated acquisition range of the unmanned aerial vehicles can be avoided, the data calculation is influenced by obtaining excessive repeated data, and the route generation efficiency is reduced. Support is provided for rapidly arriving at disaster areas to help the disaster victims.
Preferably, in any of the above embodiments, the planning module 400 is specifically configured to:
the method comprises the steps of inputting collected environment data of different areas into a prediction model to obtain road sections with risks in the areas, integrating the road sections with risks in all the areas, generating an optimal path according to integrated branching road sections, sending the optimal path to a rescue command center, and calling rescue goods and materials by the rescue command center according to the optimal path, wherein the environment data comprise collection results of remote sensing image data and collection results of geological data.
In some possible implementation manners, the risk road section in the disaster area is obtained through the prediction model, on one hand, the optimal path is generated according to the risk road section, so that the path is relatively safe, the safety of the environment where rescue workers are located is relatively high, on the other hand, the optimal path avoids the risk road section, so that the possibility of temporarily blocking the path in the rescue process is relatively low, and the rescue workers can quickly reach the disaster site for rescue.
Preferably, in any of the above embodiments, further comprising: and the repeating module is used for collecting the remote sensing image data and the geological data of the disaster area again at preset time intervals, and repeating the task of the planning module until the rescue is finished.
In some possible implementation modes, data are repeatedly acquired at preset time intervals, the optimal path is continuously monitored, and if an emergency happens to cause path blockage or the emergency is suddenly uncontrollable due to a disaster, the rescue task can be completed while casualties are reduced through adjustment of the optimal path.
Another technical solution of the present invention for solving the above technical problems is as follows: a storage medium having instructions stored therein, which when read by a computer, cause the computer to perform a method of emergency handling for a drone as claimed in any one of the preceding claims.
In some possible embodiments, the technical effect of acquiring the data in the largest range by calling the fewest unmanned aerial vehicles can be achieved by processing the basic information and calculating the number of the unmanned aerial vehicles, so that resources are saved, and meanwhile, the slow rescue speed caused by repeated data acquisition, repeated data calculation and the like is avoided; in addition, the data of the disaster area is collected through the unmanned aerial vehicle, based on the characteristics of the unmanned aerial vehicle, on one hand, casualties can not appear in the data collection process, on the other hand, the overall situation of the disaster area can be comprehensively obtained through high-altitude data collection, and compared with means such as manual investigation report or historical landform data, the efficiency and the accuracy can be greatly improved through the unmanned aerial vehicle, the rescue route can be more effectively planned according to the overall landform collection result, rescue workers can timely arrive at the rescue site, casualties of the disaster people are reduced, and meanwhile, the safety of the rescue workers is guaranteed.
Another technical solution of the present invention for solving the above technical problems is as follows: an electronic device comprising a memory, a processor and a program stored on the memory and running on the processor, wherein the processor executes the program to implement a method for emergency handling of a drone according to any one of the above.
In some possible embodiments, the technical effect of acquiring the data in the largest range by calling the fewest unmanned aerial vehicles can be achieved by processing the basic information and calculating the number of the unmanned aerial vehicles, so that resources are saved, and meanwhile, the slow rescue speed caused by repeated data acquisition, repeated data calculation and the like is avoided; in addition, the data of the disaster area is collected through the unmanned aerial vehicle, based on the characteristics of the unmanned aerial vehicle, on one hand, casualties can not appear in the data collection process, on the other hand, the overall situation of the disaster area can be comprehensively obtained through high-altitude data collection, and compared with means such as manual investigation report or historical landform data, the efficiency and the accuracy can be greatly improved through the unmanned aerial vehicle, the rescue route can be more effectively planned according to the overall landform collection result, rescue workers can timely arrive at the rescue site, casualties of the disaster people are reduced, and meanwhile, the safety of the rescue workers is guaranteed.
The reader should understand that in the description of this specification, reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means 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 are not necessarily intended to 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. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described method embodiments are merely illustrative, and for example, the division of steps into only one logical functional division may be implemented in practice in another way, for example, multiple steps may be combined or integrated into another step, or some features may be omitted, or not implemented.
The above method, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
While the invention has been described with reference to specific embodiments, 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. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An unmanned aerial vehicle emergency processing method is characterized by comprising the following steps:
step 1, acquiring basic information of a disaster area;
step 2, processing the basic information to obtain the dispatching quantity of the unmanned aerial vehicles;
step 3, acquiring remote sensing image data and geological data of the disaster area through acquisition equipment in a plurality of unmanned aerial vehicles;
and 4, planning a rescue route according to the acquisition result of the remote sensing image data and the acquisition result of the geological data.
2. The emergency processing method for unmanned aerial vehicle according to claim 1, wherein the basic information comprises: a disaster location, a disaster type, and a disaster range.
3. The unmanned aerial vehicle emergency processing method according to claim 2, wherein the step 2 is specifically:
and processing the disaster range through a multi-factor maximum coverage model to obtain the dispatching quantity of the unmanned aerial vehicles and the designated position of each unmanned aerial vehicle.
4. The unmanned aerial vehicle emergency processing method according to claim 2, wherein step 4 specifically comprises:
the method comprises the steps of inputting collected environment data of different areas into a prediction model to obtain road sections with risks in the areas, integrating the road sections with risks in all the areas, generating an optimal path according to integrated branching road sections, sending the optimal path to a rescue command center, and calling rescue goods and materials by the rescue command center according to the optimal path, wherein the environment data comprise collection results of remote sensing image data and collection results of geological data.
5. The unmanned aerial vehicle emergency processing method according to claim 4, further comprising, after step 4:
and 5, acquiring remote sensing image data and geological data of the disaster area again at preset time intervals, and repeating the step 4 until rescue is finished.
6. An unmanned aerial vehicle emergency processing system, comprising:
the acquisition module is used for acquiring basic information of a disaster area;
the processing module is used for processing the basic information to obtain the dispatching quantity of the unmanned aerial vehicles;
the acquisition module is used for acquiring remote sensing image data and geological data of the disaster area through a plurality of unmanned aerial vehicles;
and the planning module is used for planning a rescue line according to the acquisition result of the remote sensing image data and the acquisition result of the geological data.
7. The unmanned aerial vehicle emergency processing system of claim 6, wherein the basic information comprises: a disaster location, a disaster type, and a disaster range.
8. The unmanned aerial vehicle emergency processing system of claim 7, wherein the processing module is specifically configured to:
and processing the disaster range through a multi-factor maximum coverage model to obtain the dispatching quantity of the unmanned aerial vehicles and the designated position of each unmanned aerial vehicle.
9. A storage medium having stored therein instructions which, when read by a computer, cause the computer to carry out a drone emergency treatment method according to any one of claims 1 to 5.
10. An electronic device comprising a memory, a processor, and a program stored on the memory and running on the processor, wherein the processor when executing the program implements a drone emergency handling method according to any one of claims 1 to 5.
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