CN118075728B - Unmanned aerial vehicle response decision-making method and device for emergency communication scene - Google Patents
Unmanned aerial vehicle response decision-making method and device for emergency communication scene Download PDFInfo
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Abstract
The invention relates to the technical field of emergency communication, in particular to an unmanned aerial vehicle response decision-making method and device facing an emergency communication scene, wherein the method comprises the following steps: training an initial emergency communication demand prediction model by using a first training sample; determining a second training sample according to the first training sample and the trained emergency communication demand prediction model; training the initial service weight adjustment parameter prediction model by using a second training sample; according to the state information of each unit area and the emergency communication demand prediction model, the demand prediction probability of each unit area is determined, and then according to the service weight adjustment parameter prediction model, the adjustment parameters of an emergency communication service weight algorithm are determined, and then the service weight of each unit area is determined; and determining an unmanned aerial vehicle service flight path planning scheme according to the service weight of each unit area and the unmanned aerial vehicle information. By adopting the invention, the response efficiency of emergency communication can be improved.
Description
Technical Field
The invention relates to the technical field of emergency communication, in particular to an unmanned aerial vehicle response decision-making method and device for an emergency communication scene.
Background
In the face of natural disasters, the emergency communication takes on the role of "urgent pioneer" for timely, accurately and smoothly transmitting first hand information, is a central nerve for a decision maker to correctly command rescue and relief work, and the high-efficiency emergency communication can effectively reduce the life and property loss of people. The unmanned aerial vehicle base station is used for carrying out emergency communication service, so that the advantage of high-altitude communication can be fully utilized, and communication guarantee is provided for disaster areas in a more rapid and efficient mode.
Compared with a general communication scene, the emergency communication service for natural disasters is more difficult, and mainly because the emergency communication requirements of disaster area people and objects are high dynamic. For example, as the disaster progresses, a wide range of position shifts may occur in disaster areas, disaster-stricken people, and the like. Aiming at the dynamic change of the emergency communication requirement of the disaster area, the article "Resource scheduling based on deep reinforcement learning in UAV assisted emergency communication networks"（Wang C, Deng D, Xu L, et al.） provides a deep reinforcement learning algorithm based on reinforcement learning and convolutional neural network aiming at the dynamic communication requirement of the user in the emergency communication scene, and the algorithm can predict the macro base station power distribution scheme and unmanned aerial vehicle service area selection according to the current requirement scene, so that the utilization rate of spectrum resources in the emergency communication scene is effectively improved.
However, the unmanned aerial vehicle response strategy facing the emergency communication scene needs to consider the high dynamic performance of the emergency communication demands of disaster area people and things, how to intelligently predict the potential emergency communication demands of disaster areas, and further plan the unmanned aerial vehicle flight service strategy is a key for realizing high-efficiency emergency communication service. The prior art is mostly based on known emergency communication demand development research, and the problems of potential communication demand prediction, service strategy planning and the like are lack of deep discussion, so that the corresponding efficiency of emergency communication is low.
Disclosure of Invention
In order to solve the technical problem of low corresponding efficiency of emergency communication in the prior art, the embodiment of the invention provides an unmanned aerial vehicle response decision-making method and device for an emergency communication scene. The technical scheme is as follows:
In one aspect, an unmanned aerial vehicle response decision-making method facing an emergency communication scene is provided, the method is implemented by unmanned aerial vehicle response decision-making equipment facing the emergency communication scene, and the method comprises:
S1, an initial emergency communication demand prediction model is built, and a first training sample is used for training the initial emergency communication demand prediction model to obtain a trained emergency communication demand prediction model;
s2, determining a second training sample according to the first training sample and the trained emergency communication demand prediction model;
S3, constructing an initial service weight adjustment parameter prediction model, and training the initial service weight adjustment parameter prediction model by using the second training sample to obtain a trained service weight adjustment parameter prediction model;
s4, uniformly dividing the disaster area to be predicted into a plurality of unit areas, and acquiring state information of each unit area;
s5, determining the demand prediction probability of each unit area according to the state information of each unit area and the trained emergency communication demand prediction model;
S6, determining the adjusting parameters of the emergency communication service weight algorithm according to the demand forecasting probability of each unit area and the trained service weight adjusting parameter forecasting model;
S7, determining the service weight of each unit area according to the adjustment parameters of the emergency communication service weight algorithm, the demand prediction probability of each unit area and the emergency communication service weight algorithm;
S8, determining an unmanned aerial vehicle service flight path planning scheme according to the service weight of each unit area and the unmanned aerial vehicle information.
On the other hand, an unmanned aerial vehicle response decision-making device facing emergency communication scene is provided, the device is applied to unmanned aerial vehicle response decision-making method facing emergency communication scene, the device includes:
The first training module is used for constructing an initial emergency communication demand prediction model, and training the initial emergency communication demand prediction model by using a first training sample to obtain a trained emergency communication demand prediction model;
the first determining module is used for determining a second training sample according to the first training sample and the trained emergency communication demand prediction model;
The second training module is used for constructing an initial service weight adjustment parameter prediction model, and training the initial service weight adjustment parameter prediction model by using the second training sample to obtain a trained service weight adjustment parameter prediction model;
The acquisition module is used for uniformly dividing the disaster area to be predicted into a plurality of unit areas and acquiring the state information of each unit area;
The second determining module is used for determining the demand prediction probability of each unit area according to the state information of each unit area and the trained emergency communication demand prediction model;
The third determining module is used for determining the adjusting parameters of the emergency communication service weight algorithm according to the demand prediction probability of each unit area and the trained service weight adjusting parameter prediction model;
the fourth determining module is used for determining the service weight of each unit area according to the adjusting parameter of the emergency communication service weight algorithm, the demand prediction probability of each unit area and the emergency communication service weight algorithm;
and the fifth determining module is used for determining an unmanned aerial vehicle service flight path planning scheme according to the service weight of each unit area and the unmanned aerial vehicle information.
On the other hand, provide an unmanned aerial vehicle response decision-making equipment towards emergent communication scene, unmanned aerial vehicle response decision-making equipment towards emergent communication scene includes: a processor; and the memory is stored with computer readable instructions which, when executed by the processor, implement any one of the unmanned aerial vehicle response decision methods facing the emergency communication scene.
In another aspect, a computer readable storage medium is provided, in which at least one instruction is stored, loaded and executed by a processor to implement any one of the above-described unmanned aerial vehicle response decision-making methods for emergency communication scenarios.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
The potential emergency communication requirements of the disaster area are intelligently predicted, and then the unmanned aerial vehicle flight service path is intelligently planned. In addition, aiming at the problem of the association mechanism of communication demand prediction and service path planning in the unmanned aerial vehicle intelligent response decision process, the invention provides an emergency communication service weight calculation method, which realizes the intelligent decision association between the demand prediction and the path planning. In addition, aiming at the problem of adjusting parameter calculation in the emergency communication service weight calculation process, the invention provides a service weight adjusting parameter calculation method, which realizes automatic calculation of the emergency communication service weight and improves response efficiency of emergency communication.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an unmanned aerial vehicle response decision-making method facing an emergency communication scene provided by an embodiment of the invention;
Fig. 2 is a flow chart of an unmanned aerial vehicle response decision-making method facing an emergency communication scene, which is provided by the embodiment of the invention;
Fig. 3 is a block diagram of an unmanned aerial vehicle response decision-making device facing an emergency communication scene, which is provided by the embodiment of the invention;
Fig. 4 is a schematic structural diagram of an unmanned aerial vehicle response decision-making device facing an emergency communication scene provided by the embodiment of the invention.
Detailed Description
The technical scheme of the invention is described below with reference to the accompanying drawings.
In embodiments of the invention, words such as "exemplary," "such as" and the like are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, the term use of an example is intended to present concepts in a concrete fashion. Furthermore, in embodiments of the present invention, the meaning of "and/or" may be that of both, or may be that of either, optionally one of both.
In the embodiments of the present invention, "image" and "picture" may be sometimes used in combination, and it should be noted that the meaning of the expression is consistent when the distinction is not emphasized. "of", "corresponding (corresponding, relevant)" and "corresponding (corresponding)" are sometimes used in combination, and it should be noted that the meaning of the expression is consistent when the distinction is not emphasized.
In embodiments of the present invention, sometimes a subscript such as W 1 may be written in a non-subscript form such as W1, and the meaning of the expression is consistent when de-emphasizing the distinction.
In order to make the technical problems, technical solutions and advantages to be solved more apparent, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
The embodiment of the invention provides an unmanned aerial vehicle response decision-making method facing an emergency communication scene, which can be realized by unmanned aerial vehicle response decision-making equipment facing the emergency communication scene, wherein the unmanned aerial vehicle response decision-making equipment facing the emergency communication scene can be a terminal or a server. As shown in the flow chart of the unmanned aerial vehicle response decision-making method facing the emergency communication scene in fig. 1, and as shown in the flow chart of the unmanned aerial vehicle response decision-making method facing the emergency communication scene in fig. 2, the processing flow of the method may include the following steps:
S1, an initial emergency communication demand prediction model is built, and a first training sample is used for training the initial emergency communication demand prediction model to obtain a trained emergency communication demand prediction model.
Optionally, the first training sample is represented by the following formula (1):
（1）
Wherein, Is the firstIn the service round, the actual value of the input characteristic variable of the kth unit area of the sample disaster area, namely sample state information; Is the first In the service round, the actual value of the output variable of the kth unit area of the sample disaster area, namely the sample demand probability true value; is the total number of service rounds contained in the first training sample, Is the total number of divided unit areas in the sample disaster area.
In a possible implementation, an initial emergency communication demand prediction model can be constructed based on a machine learning algorithmThe following formula (2) shows:
（2）
Wherein, The method is characterized in that the method is an input characteristic variable of an initial emergency communication demand prediction model, wherein the input characteristic variable comprises geographic coordinates, altitude, temperature, humidity, wind power, pressure, illumination and volume; Is the output variable of the model, namely each unit area in the disaster area Model training data sets to be used if unknown communication needs existAs shown in the above formula (1).
Representing estimated values of output variables, i.e. model predicted unit areasThe probability of unknown communication demand exists, simply called demand prediction probability, and the training goal of the model is to make the estimated value of the output variableAnd actual valueIs the smallest. Thus, after model training, the model can be trained according to the input variables before a new service round startsCalculation ofDemand forecast probability of (2)The following formula (3) shows:
（3）
it should be noted that, the input feature variables of the emergency communication demand prediction model are not limited to the above-mentioned one, and may also include data obtained by monitoring data by other physical sensors, such as people flow density data, image monitoring data, etc., and the user may select the input feature variables according to the demand, and the specific implementation manner after changing the input feature variables may still refer to the above-mentioned steps, which is not described in detail in the embodiment of the present invention.
It should be noted that the foregoing may be a machine learning model commonly used in the prior art, such as a deep neural network (Deep Neural Network, DNN) or a convolutional neural network (Convolutional Neural Network, CNN), etc., and the operation principle and specific training manner thereof may be referred to the conventional usage in the prior art, which is not repeated in the embodiments of the present invention.
S2, determining a second training sample according to the first training sample and the trained emergency communication demand prediction model.
The second training samples may include a sample demand prediction estimation probability, a model evaluation index, and a sample adjustment parameter.
Optionally, determining the second training sample according to the first training sample and the trained emergency communication requirement prediction model in S2 may include the following steps S21-S24:
S21, inputting sample state information in the first training sample into a trained emergency communication demand prediction model to obtain sample demand prediction estimation probability, and recording model evaluation indexes of the trained emergency communication demand prediction model.
In a possible implementation, the specific operation of obtaining the sample demand prediction estimation probability and the model evaluation index according to the first training sample may be as shown in algorithm 1 in table 1 below:
TABLE 1
Wherein the input of algorithm 1 is the data set of the first training dataThe output is a modelPerformance evaluation index of (a)Model and moldIs the prediction result of (2)Wherein the performance evaluation indexThe calculation and recording modes of the method can be the accuracy of a model or the Area Under the Curve (AUC) commonly used in the prior art, and the like, and can be selected by a user according to the needs, and the calculation and recording modes can refer to the conventional operation in the prior art, so that the embodiment of the invention is not repeated.
S22, predicting the estimated probability and presetting the adjustment parameters based on the sample requirements, and obtaining the intermediate service weight according to the emergency communication service weight algorithm.
In a possible implementation manner, a preset adjustment parameter is obtained, the preset adjustment parameter is put into an emergency communication service weight algorithm, an intermediate service weight corresponding to the sample demand prediction estimation probability is calculated, and the emergency communication service weight algorithm can be referred to a following formula (10).
S23, according to the intermediate service weight and the sample unmanned aerial vehicle information, the emergency traffic service demand of the sample unit area in the sample unmanned aerial vehicle service flight path planning scheme and the sample unmanned aerial vehicle flight scheduling scheme are obtained.
In a possible implementation manner, according to the intermediate service weight and the sample unmanned aerial vehicle information, the emergency traffic service demand of the sample unit area in the sample unmanned aerial vehicle service flight path planning scheme and the sample unmanned aerial vehicle flight scheduling scheme are obtained, and the calculation method can be referred to the following formulas (11) - (16), which are not repeated here.
S24, constructing an optimization problem of maximizing total service demand through the emergency traffic service demand of the sample unit area and the sample unmanned aerial vehicle flight scheduling scheme, and solving the optimization problem through a heuristic algorithm to obtain sample adjustment parameters.
Optionally, constructing the optimization problem of maximizing the total service demand through the emergency traffic service demand of the sample unit area and the sample unmanned aerial vehicle flight scheduling scheme in S24 may include the following steps S241-S242:
S241, calculating the total service demand according to the emergency traffic service demand of the sample unit area, the sample unmanned aerial vehicle flight scheduling scheme and the following formula (4):
（4）
Wherein, Indicating the total amount of service demand,Representing the flight scheduling scheme of the mth unmanned aerial vehicle in the kth sample unit area at the time slot N, wherein N is the total number of time slots,To indicate the function, ifIs shown inThe mth unmanned aerial vehicle flies to the upper part of the kth sample unit area in the round service and provides emergency communication service, thenOtherwise；Representing emergency traffic service demand in a kth sample unit area; s242, constructing an optimization problem of maximizing the total service demand according to the formula (4), as shown in the following formulas (5) - (6):
（5）
（6）
Wherein, For the service weight adjusting parameter, the value range is; Adjusting parameters by optimizing service weights，Representing the total amount of service demand.
In a possible implementation, a heuristic algorithm may be used to solveFor example, particle swarm algorithm, simulated annealing algorithm, etc., the solving algorithm may be a method commonly used in the prior art, and this will not be described in detail in the embodiments of the present invention.
S3, constructing an initial service weight adjustment parameter prediction model, and training the initial service weight adjustment parameter prediction model by using a second training sample to obtain a trained service weight adjustment parameter prediction model.
In a possible implementation manner, when emergency communication service weight (or simply referred to as service weight) of each unit area is set, if the demand prediction probability of the kth unit area is setThe unmanned aerial vehicle flight service path is directly planned as the service weight, so that the problems that the unmanned aerial vehicle has too high invalid flight proportion, the actual communication requirement is ignored and the like can be caused, and the response efficiency of the emergency communication service is affected. Therefore, the embodiment of the invention provides an emergency communication service weight algorithm, and the service weight is obtained after intelligent adjustment and correction of the demand prediction probability. The adjustment parameters of the emergency communication service weight algorithm need to be calculated to ensure the accuracy of the emergency communication service weight algorithm, so that the embodiment of the invention provides a service weight adjustment parameter prediction model, and the adjustment parameters of the emergency communication service weight algorithm are accurately predicted by using the model.
Optionally, training the initial service weight adjustment parameter prediction model by using the second training sample in S3 to obtain a trained service weight adjustment parameter prediction model may include the following steps S31-S32:
S31, inputting the sample demand prediction estimation probability and the model evaluation index into an initial service weight adjustment parameter prediction model to obtain a predicted value of the service weight adjustment parameter.
S32, comparing the predicted value of the service weight adjusting parameter with the sample adjusting parameter by using the MSE loss function, and adjusting the parameters in the initial service weight adjusting parameter prediction model according to the comparison result until the loss converges, so as to obtain the trained service weight adjusting parameter prediction model.
In a possible implementation, an initial service weight adjustment parameter prediction model constructed based on a machine learning algorithmCan be represented by the following formula (7):
（7）
Wherein, Adjusting parameter prediction model inputs for initial service weights, including the output results of algorithm 1，；Is the model output, i.e. solved in the optimization problem (5). Thus, the data set used for model training isThe following formula is shown:
（8）
Wherein, ，Is the output of algorithm 1, i.e. the modelInput variableIs the actual value of (2); Is the result of the solution of the optimization problem (5), i.e. the model Output variableIs the actual value of (2); is the total number of service rounds contained in the sample dataset.
Representing the estimated value of the output variable, i.e. the model predicted service weight adjustment parameter, the training of the model is aimed at letting the estimated value of the output variableAnd actual valueThe MSE of (c) is the smallest,Multiple mainstream deep neural network algorithms may be selected. Thus, after model training, the model can be trained according to the input variables before a new service round startsAndEstimating service weight adjustment parametersThe following formula is shown:
（9）。
s4, uniformly dividing the disaster area to be predicted into a plurality of unit areas, and acquiring state information of each unit area.
In a possible implementation manner, the disaster area to be predicted is uniformly divided into a plurality of unit areas, k can be used for representing the sequence of the unit areas, and the state information of each unit area is the input characteristic variables of the emergency communication demand prediction model, such as physical sensor monitoring data of geographic coordinates, altitude, temperature, humidity, wind power, pressure, illumination, volume and the like, or is selected by the user according to the demand, and the state information of each unit area is consistent with the sample input in the model training process in the step S1.
S5, determining the demand prediction probability of each unit area according to the state information of each unit area and the trained emergency communication demand prediction model.
In a possible implementation manner, the state information of each unit area is input into the trained emergency communication demand prediction model, and the formula for calculating the demand prediction probability of each unit area can be referred to the formula (3) in the step S1, which is not described herein.
S6, according to the demand prediction probability of each unit area and the trained service weight adjustment parameter prediction model, determining the adjustment parameters of the emergency communication service weight algorithm.
In a feasible implementation mode, the demand prediction probability of each unit area is input into a trained service weight adjustment parameter prediction model, and the model output is the adjustment parameter of the emergency communication service weight algorithm.
And S7, determining the service weight of each unit area according to the adjustment parameters of the emergency communication service weight algorithm, the demand prediction probability of each unit area and the emergency communication service weight algorithm.
Optionally, the determining the service weight of each unit area according to the adjustment parameter of the emergency communication service weight algorithm, the demand prediction probability of each unit area and the emergency communication service weight algorithm in S7 may be as follows:
Determining the service weight of each unit area according to the adjustment parameters of the emergency communication service weight algorithm, the demand prediction probability of each unit area, the emergency communication service weight algorithm and the following formula (10):
（5）
Wherein, Is the service weight of the kth unit area,Is the demand prediction probability of the kth unit area,Is the demand prediction probability of the ith unit area; Is the adjustment parameter, K' represents the total number of unit areas.
It should be noted that the number of the substrates,The larger the algorithm isThe larger the relative service weight set in the high unit area is, the more different the service weight of each unit area is; in contrast to this,The closer to 0, the closer the algorithm sets the service weight for the unit area map.
S8, determining an unmanned aerial vehicle service flight path planning scheme according to the service weight of each unit area and the unmanned aerial vehicle information.
Optionally, determining the unmanned aerial vehicle service flight path planning scheme according to the service weight of each unit area and the unmanned aerial vehicle information in S8, and the specific operation may be as follows:
According to the service weight of each unit area and the unmanned aerial vehicle information, taking the total amount of the maximized service weight as an optimization target, modeling an unmanned aerial vehicle path planning problem, wherein the following formulas (11) - (16) are adopted:
（11）
（12）
（13）
（14）
（15）
（16）
Wherein, Is the total amount of service weight; for unmanned aerial vehicle flight scheduling scheme, if Representing in time slotsWhen the mth unmanned plane flies to the upper part of the kth unit area and provides emergency communication service, and otherwise；The flight path is serviced for the unmanned aerial vehicle,Indicating that the mth unmanned plane is in a time slotIs a flying position of (2); to maximize the discrete segment length, M represents the total number of unmanned aerial vehicles, M and M' represent different orders of unmanned aerial vehicles respectively, and the fastest flight speed of the unmanned aerial vehicle is The service duration of the unmanned aerial vehicle is as followsWill beDivided intoEach time slot is expressed asThe time slot length is，Is the flight safety distance between unmanned aerial vehicles.
In the above formula, formula (12) is the total amount of service weightThe constraint condition (13) represents unmanned aerial vehicle discrete flight distance constraint, the constraint conditions (14) and (15) represent unmanned aerial vehicle flight scheduling scheme constraint, and the constraint condition (16) represents inter-unmanned aerial vehicle flight safety distance constraint. For the specific expression form of the model, the problem can be solved by using a continuous convex approximation method (Successive Convex Approximation, SCA), a block coordinate descent method (Block Coordinate Descent, BCD) and other methods, and the specific solving mode can refer to a conventional solving mode in the prior art, so that the embodiment of the invention is not repeated here. After the solution, an unmanned aerial vehicle service flight path planning scheme can be obtained, and flight control is carried out on the unmanned aerial vehicle according to the unmanned aerial vehicle service flight path planning scheme.
According to the embodiment of the invention, the potential emergency communication requirements of the disaster area are intelligently predicted, so that the flight service path of the unmanned aerial vehicle is intelligently planned. In addition, aiming at the problem of the association mechanism of communication demand prediction and service path planning in the unmanned aerial vehicle intelligent response decision process, the invention provides an emergency communication service weight calculation method, which realizes the intelligent decision association between the demand prediction and the path planning. In addition, aiming at the problem of adjusting parameter calculation in the emergency communication service weight calculation process, the invention provides a service weight adjusting parameter calculation method, which realizes automatic calculation of the emergency communication service weight and improves response efficiency of emergency communication.
Fig. 3 is a block diagram of an unmanned aerial vehicle response decision device for an emergency communication scenario, which is used for an unmanned aerial vehicle response decision method for an emergency communication scenario, according to an exemplary embodiment. Referring to fig. 3, the apparatus includes:
The first training module 310 is configured to construct an initial emergency communication demand prediction model, and train the initial emergency communication demand prediction model by using a first training sample to obtain a trained emergency communication demand prediction model;
A first determining module 320, configured to determine a second training sample according to the first training sample and the trained emergency communication requirement prediction model;
A second training module 330, configured to construct an initial service weight adjustment parameter prediction model, and train the initial service weight adjustment parameter prediction model by using the second training sample to obtain a trained service weight adjustment parameter prediction model;
an obtaining module 340, configured to uniformly divide a disaster area to be predicted into a plurality of unit areas, and obtain status information of each unit area;
The second determining module 350 is configured to determine a demand prediction probability of each unit area according to the status information of each unit area and the trained emergency communication demand prediction model;
A third determining module 360, configured to determine an adjustment parameter of the emergency communication service weight algorithm according to the demand prediction probability of each unit area and the trained service weight adjustment parameter prediction model;
a fourth determining module 370, configured to determine a service weight of each unit area according to the adjustment parameter of the emergency communication service weight algorithm, the demand prediction probability of each unit area, and the emergency communication service weight algorithm;
and a fifth determining module 380, configured to determine an unmanned aerial vehicle service flight path planning scheme according to the service weight of each unit area and the unmanned aerial vehicle information.
Optionally, the first training sample is represented by the following formula (1):
（1）
Wherein, Is the firstIn the service round, the actual value of the input characteristic variable of the kth unit area of the sample disaster area, namely sample state information; Is the first In the service round, the actual value of the output variable of the kth unit area of the sample disaster area, namely the sample demand probability true value; is the total number of service rounds contained in the first training sample, Is the total number of divided unit areas in the sample disaster area.
Optionally, the second training samples comprise sample demand prediction estimation probabilities, model evaluation indexes and sample adjustment parameters;
The first determining module 320 is configured to:
S21, inputting sample state information in the first training sample into a trained emergency communication demand prediction model to obtain sample demand prediction estimation probability, and recording model evaluation indexes of the trained emergency communication demand prediction model;
s22, predicting an estimated probability and preset adjustment parameters based on sample requirements, and obtaining an intermediate service weight according to an emergency communication service weight algorithm;
s23, obtaining the emergency passing service demand of a sample unit area in a sample unmanned aerial vehicle service flight path planning scheme and a sample unmanned aerial vehicle flight scheduling scheme according to the intermediate service weight and the sample unmanned aerial vehicle information;
s24, constructing an optimization problem of maximizing total service demand through the emergency passing service demand of the sample unit area and the sample unmanned aerial vehicle flight scheduling scheme, and solving the optimization problem through a heuristic algorithm to obtain sample adjustment parameters.
Optionally, the first determining module 320 is configured to:
S241, calculating the total service demand according to the emergency traffic service demand of the sample unit area, the sample unmanned aerial vehicle flight scheduling scheme and the following formula (2):
（2）
Wherein, Indicating the total amount of service demand,Representing the flight scheduling scheme of the mth unmanned aerial vehicle in the kth sample unit area at the time slot N, wherein N is the total number of time slots,To indicate the function, ifIs shown inThe mth unmanned aerial vehicle flies to the upper part of the kth sample unit area in the round service and provides emergency communication service, thenOtherwise；Representing emergency traffic service demand in a kth sample unit area;
S242, constructing an optimization problem of maximizing the total service demand according to the formula, wherein the optimization problem is shown in the following formulas (3) and (4):
（3）
（4）
Wherein, For the service weight adjusting parameter, the value range is; Adjusting parameters by optimizing service weights，Representing the total amount of service demand.
Optionally, the second training module 330 is configured to:
S31, inputting the sample demand prediction estimation probability and the model evaluation index into an initial service weight adjustment parameter prediction model to obtain a predicted value of the service weight adjustment parameter;
S32, comparing the predicted value of the service weight adjusting parameter with the sample adjusting parameter by using the MSE loss function, and adjusting the parameters in the initial service weight adjusting parameter prediction model according to the comparison result until the loss converges, so as to obtain the trained service weight adjusting parameter prediction model.
Optionally, the fourth determining module 370 is configured to:
Determining the service weight of each unit area according to the adjusting parameters of the emergency communication service weight algorithm, the demand prediction probability of each unit area, the emergency communication service weight algorithm and the following formula (5):
（5）
Wherein, Is the service weight of the kth unit area,Is the demand prediction probability of the kth unit area,Is the adjustment parameter, K' represents the total number of unit areas.
Optionally, the fifth determining module 380 is configured to:
According to the service weight of each unit area and the unmanned aerial vehicle information, modeling an unmanned aerial vehicle path planning problem by taking the total amount of maximized service weight as an optimization target, wherein the modeling is as shown in the following formulas (6) - (11):
（6）
（7）
（8）
（9）
（10）
（11）
Wherein, Is the total amount of service weight; for unmanned aerial vehicle flight scheduling scheme, if Representing in time slotsWhen the mth unmanned plane flies to the upper part of the kth unit area and provides emergency communication service, and otherwise；The flight path is serviced for the unmanned aerial vehicle,Indicating that the mth unmanned plane is in a time slotIs a flying position of (2); To maximize the discrete segment length, M represents the total number of drones, M and M' represent the different orders of drones, The fastest flight speed of the unmanned aerial vehicle is represented, and the service duration of the unmanned aerial vehicle is as followsWill beDivided intoEach time slot is expressed asThe time slot length is，Is the flight safety distance between unmanned aerial vehicles.
According to the embodiment of the invention, the potential emergency communication requirements of the disaster area are intelligently predicted, so that the flight service path of the unmanned aerial vehicle is intelligently planned. In addition, aiming at the problem of the association mechanism of communication demand prediction and service path planning in the unmanned aerial vehicle intelligent response decision process, the invention provides an emergency communication service weight calculation method, which realizes the intelligent decision association between the demand prediction and the path planning. In addition, aiming at the problem of adjusting parameter calculation in the emergency communication service weight calculation process, the invention provides a service weight adjusting parameter calculation method, which realizes automatic calculation of the emergency communication service weight and improves response efficiency of emergency communication.
Fig. 4 is a schematic structural diagram of an unmanned aerial vehicle response decision device facing an emergency communication scene, which is provided by the embodiment of the present invention, and as shown in fig. 4, the unmanned aerial vehicle response decision device facing the emergency communication scene may include the unmanned aerial vehicle response decision device facing the emergency communication scene shown in fig. 3. Optionally, the unmanned aerial vehicle response decision device 410 for emergency communication scenarios may comprise a first processor 2001.
Optionally, the unmanned aerial vehicle response decision device 410 for emergency communication scenarios may further comprise a memory 2002 and a transceiver 2003.
The first processor 2001 may be connected to the memory 2002 and the transceiver 2003, for example, via a communication bus.
The following describes the various components of the drone response decision device 410 facing the emergency communication scenario in detail with reference to fig. 4:
The first processor 2001 is a control center of the unmanned aerial vehicle response decision device 410 facing the emergency communication scene, and may be one processor or a generic name of a plurality of processing elements. For example, the first processor 2001 is one or more central processing units (central processing unit, CPU), may be an Application SPECIFIC INTEGRATED Circuit (ASIC), or may be one or more integrated circuits configured to implement embodiments of the present invention, such as: one or more microprocessors (DIGITAL SIGNAL processors, DSPs), or one or more field programmable gate arrays (field programmable GATE ARRAY, FPGAs).
Alternatively, the first processor 2001 may perform various functions of the drone response decision device 410 for emergency communication scenarios by running or executing a software program stored in the memory 2002, and invoking data stored in the memory 2002.
In a specific implementation, first processor 2001 may include one or more CPUs, such as CPU0 and CPU1 shown in fig. 4, as an example.
In a specific implementation, as an embodiment, the unmanned aerial vehicle response decision device 410 facing the emergency communication scenario may also include a plurality of processors, such as the first processor 2001 and the second processor 2004 shown in fig. 4. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).
The memory 2002 is used for storing a software program for executing the solution of the present invention, and is controlled by the first processor 2001 to execute the solution, and the specific implementation may refer to the above method embodiment, which is not described herein.
Alternatively, memory 2002 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-only memory, EEPROM), compact disc read-only memory (compact disc read-only memory) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, without limitation. The memory 2002 may be integrated with the first processor 2001, may exist separately, and may be coupled to the first processor 2001 through an interface circuit (not shown in fig. 4) of the drone response decision device 410 for emergency communication scenarios, which is not particularly limited in this embodiment of the present invention.
A transceiver 2003 for communicating with a network device or with a terminal device.
Alternatively, transceiver 2003 may include a receiver and a transmitter (not separately shown in fig. 4). The receiver is used for realizing the receiving function, and the transmitter is used for realizing the transmitting function.
Alternatively, the transceiver 2003 may be integrated with the first processor 2001, or may exist separately, and be coupled to the first processor 2001 through an interface circuit (not shown in fig. 4) of the unmanned aerial vehicle response decision-making device 410 facing the emergency communication scenario, which is not particularly limited in this embodiment of the present invention.
It should be noted that the structure of the unmanned aerial vehicle response decision device 410 facing the emergency communication scenario shown in fig. 4 is not limited to this router, and the actual knowledge structure recognition device may include more or fewer components than shown, or may combine some components, or may be a different arrangement of components.
In addition, the technical effects of the unmanned aerial vehicle response decision-making device 410 facing the emergency communication scene may refer to the technical effects of the unmanned aerial vehicle response decision-making method facing the emergency communication scene described in the above method embodiment, and will not be described herein.
It is to be appreciated that the first processor 2001 in embodiments of the invention may be a central processing unit (central processing unit, CPU) which may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL processors, DSPs), application Specific Integrated Circuits (ASICs), off-the-shelf programmable gate arrays (field programmable GATE ARRAY, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It should also be appreciated that the memory in embodiments of the present invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an erasable programmable ROM (erasable PROM), an electrically erasable programmable EPROM (EEPROM), or a flash memory. The volatile memory may be random access memory (random access memory, RAM) which acts as external cache memory. By way of example, and not limitation, many forms of random access memory (random access memory, RAM) are available, such as static random access memory (STATIC RAM, SRAM), dynamic Random Access Memory (DRAM), synchronous Dynamic Random Access Memory (SDRAM), double data rate synchronous dynamic random access memory (double DATA RATE SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (ENHANCED SDRAM, ESDRAM), synchronous link dynamic random access memory (SYNCHLINK DRAM, SLDRAM), and direct memory bus random access memory (direct rambus RAM, DR RAM).
The above embodiments may be implemented in whole or in part by software, hardware (e.g., circuitry), firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that the term "and/or" is merely an association relationship describing the associated object, and means that three relationships may exist, for example, a and/or B may mean: there are three cases, a alone, a and B together, and B alone, wherein a, B may be singular or plural. In addition, the character "/" herein generally indicates that the associated object is an "or" relationship, but may also indicate an "and/or" relationship, and may be understood by referring to the context.
In the present invention, "at least one" means one or more, and "a plurality" means two or more. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b, or c may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus, device and unit described above may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another device, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, 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 this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. 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 response decision-making method facing emergency communication scenes is characterized by comprising the following steps:
S1, an initial emergency communication demand prediction model is built, and a first training sample is used for training the initial emergency communication demand prediction model to obtain a trained emergency communication demand prediction model;
s2, determining a second training sample according to the first training sample and the trained emergency communication demand prediction model;
S3, constructing an initial service weight adjustment parameter prediction model, and training the initial service weight adjustment parameter prediction model by using the second training sample to obtain a trained service weight adjustment parameter prediction model;
s4, uniformly dividing the disaster area to be predicted into a plurality of unit areas, and acquiring state information of each unit area;
s5, determining the demand prediction probability of each unit area according to the state information of each unit area and the trained emergency communication demand prediction model;
S6, determining the adjusting parameters of the emergency communication service weight algorithm according to the demand forecasting probability of each unit area and the trained service weight adjusting parameter forecasting model;
S7, determining the service weight of each unit area according to the adjustment parameters of the emergency communication service weight algorithm, the demand prediction probability of each unit area and the emergency communication service weight algorithm;
S8, determining an unmanned aerial vehicle service flight path planning scheme according to the service weight of each unit area and the unmanned aerial vehicle information.
2. The unmanned aerial vehicle response decision-making method for an emergency communication scene according to claim 1, wherein the first training sample is represented by the following formula (1):
（1）
Wherein, Is the firstIn the service round, the actual value of the input characteristic variable of the kth unit area of the sample disaster area, namely sample state information; Is the first In the service round, the actual value of the output variable of the kth unit area of the sample disaster area, namely the sample demand probability true value; is the total number of service rounds contained in the first training sample, Is the total number of divided unit areas in the sample disaster area.
3. The unmanned aerial vehicle response decision-making method for emergency communication scenarios according to claim 2, wherein the second training samples comprise sample demand prediction estimation probabilities, model evaluation indexes and sample adjustment parameters;
and S2, determining a second training sample according to the first training sample and the trained emergency communication demand prediction model, wherein the determining comprises the following steps:
S21, inputting sample state information in the first training sample into a trained emergency communication demand prediction model to obtain sample demand prediction estimation probability, and recording model evaluation indexes of the trained emergency communication demand prediction model;
s22, predicting an estimated probability and preset adjustment parameters based on sample requirements, and obtaining an intermediate service weight according to an emergency communication service weight algorithm;
s23, obtaining the emergency passing service demand of a sample unit area in a sample unmanned aerial vehicle service flight path planning scheme and a sample unmanned aerial vehicle flight scheduling scheme according to the intermediate service weight and the sample unmanned aerial vehicle information;
s24, constructing an optimization problem of maximizing total service demand through the emergency passing service demand of the sample unit area and the sample unmanned aerial vehicle flight scheduling scheme, and solving the optimization problem through a heuristic algorithm to obtain sample adjustment parameters.
4. The unmanned aerial vehicle response decision-making method for the emergency communication scene according to claim 3, wherein the constructing the optimization problem of maximizing the total service demand by the emergency traffic service demand of the sample unit area and the sample unmanned aerial vehicle flight scheduling scheme in S24 comprises:
S241, calculating the total service demand according to the emergency traffic service demand of the sample unit area, the sample unmanned aerial vehicle flight scheduling scheme and the following formula (2):
（2）
Wherein, Indicating the total amount of service demand,Representing the flight scheduling scheme of the mth unmanned aerial vehicle in the kth sample unit area at the time slot N, wherein N is the total number of time slots,To indicate the function, ifIs shown inThe mth unmanned aerial vehicle flies to the upper part of the kth sample unit area in the round service and provides emergency communication service, thenOtherwise；Representing emergency traffic service demand in a kth sample unit area;
S242, constructing an optimization problem of maximizing the total service demand according to the formula (2), as shown in the following formulas (3) and (4):
（3）
（4）
Wherein, For the service weight adjusting parameter, the value range is; Adjusting parameters by optimizing service weights，Representing the total amount of service demand.
5. The unmanned aerial vehicle response decision-making method for emergency communication scenarios according to claim 3, wherein the training the initial service weight adjustment parameter prediction model by using the second training sample in S3 to obtain a trained service weight adjustment parameter prediction model comprises:
S31, inputting the sample demand prediction estimation probability and the model evaluation index into an initial service weight adjustment parameter prediction model to obtain a predicted value of the service weight adjustment parameter;
S32, comparing the predicted value of the service weight adjusting parameter with the sample adjusting parameter by using the MSE loss function, and adjusting the parameters in the initial service weight adjusting parameter prediction model according to the comparison result until the loss converges, so as to obtain the trained service weight adjusting parameter prediction model.
6. The unmanned aerial vehicle response decision-making method for the emergency communication scene according to claim 1, wherein the determining the service weight of each unit area according to the adjustment parameter of the emergency communication service weight algorithm, the demand prediction probability of each unit area, and the emergency communication service weight algorithm in S7 comprises:
Determining the service weight of each unit area according to the adjusting parameters of the emergency communication service weight algorithm, the demand prediction probability of each unit area, the emergency communication service weight algorithm and the following formula (5):
（5）
Wherein, Is the service weight of the kth unit area,Is the demand prediction probability of the kth unit area,Is the demand prediction probability of the ith unit area; Is the adjustment parameter, K' represents the total number of unit areas.
7. The unmanned aerial vehicle response decision-making method for the emergency communication scene according to claim 1, wherein the determining the unmanned aerial vehicle service flight path planning scheme according to the service weight of each unit area and the unmanned aerial vehicle information in S8 comprises: according to the service weight of each unit area and the unmanned aerial vehicle information, modeling an unmanned aerial vehicle path planning problem by taking the total amount of maximized service weight as an optimization target, wherein the modeling is as shown in the following formulas (6) - (11):
（6）
（7）
（8）
（9）
（10）
（11）
Wherein, Is the total amount of service weight; for unmanned aerial vehicle flight scheduling scheme, if Representing in time slotsWhen the mth unmanned plane flies to the upper part of the kth unit area and provides emergency communication service, and otherwise；The flight path is serviced for the unmanned aerial vehicle,Indicating that the mth unmanned plane is in a time slotIs a flying position of (2); To maximize the discrete segment length, M represents the total number of drones, M and M' represent the different orders of drones, The fastest flight speed of the unmanned aerial vehicle is represented, and the service duration of the unmanned aerial vehicle is as followsWill beDivided intoEach time slot is expressed asThe time slot length is，Is the flight safety distance between unmanned aerial vehicles.
8. An emergency communication scene oriented unmanned aerial vehicle response decision device for implementing the emergency communication scene oriented unmanned aerial vehicle response decision method according to any one of claims 1 to 7, characterized in that the device comprises:
The first training module is used for constructing an initial emergency communication demand prediction model, and training the initial emergency communication demand prediction model by using a first training sample to obtain a trained emergency communication demand prediction model;
the first determining module is used for determining a second training sample according to the first training sample and the trained emergency communication demand prediction model;
The second training module is used for constructing an initial service weight adjustment parameter prediction model, and training the initial service weight adjustment parameter prediction model by using the second training sample to obtain a trained service weight adjustment parameter prediction model;
The acquisition module is used for uniformly dividing the disaster area to be predicted into a plurality of unit areas and acquiring the state information of each unit area;
The second determining module is used for determining the demand prediction probability of each unit area according to the state information of each unit area and the trained emergency communication demand prediction model;
The third determining module is used for determining the adjusting parameters of the emergency communication service weight algorithm according to the demand prediction probability of each unit area and the trained service weight adjusting parameter prediction model;
the fourth determining module is used for determining the service weight of each unit area according to the adjusting parameter of the emergency communication service weight algorithm, the demand prediction probability of each unit area and the emergency communication service weight algorithm;
and the fifth determining module is used for determining an unmanned aerial vehicle service flight path planning scheme according to the service weight of each unit area and the unmanned aerial vehicle information.
9. Unmanned aerial vehicle response decision-making equipment towards emergent communication scene, its characterized in that, unmanned aerial vehicle response decision-making equipment towards emergent communication scene includes:
A processor;
a memory having stored thereon computer readable instructions which, when executed by the processor, implement the method of any of claims 1 to 7.
10. A computer readable storage medium having stored therein program code which is callable by a processor to perform the method of any one of claims 1 to 7.
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