CN116167529A - Target unmanned aerial vehicle weight prediction method, device and storage medium - Google Patents
Target unmanned aerial vehicle weight prediction method, device and storage medium Download PDFInfo
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Abstract
The embodiment of the application discloses a method, a device and a storage medium for predicting the weight of a target unmanned aerial vehicle, wherein the method comprises the following steps: generating a first weight value of the target unmanned aerial vehicle based on the fitting model; generating a second weight value of the target unmanned aerial vehicle based on the motor rotating speed corresponding to each horn of the target unmanned aerial vehicle and the rotating speed model; acquiring a first weight value generated based on a fitting model, a second weight value generated based on a rotating speed model and a weighting coefficient adopted by a prediction model, wherein the prediction model is used for predicting each weight value corresponding to the situation that a target unmanned aerial vehicle carries a load with different load weights; and inputting the weighting coefficient, the first weight value and the second weight value into a prediction model for prediction, and outputting a corresponding prediction result, wherein the prediction result comprises each prediction weight value corresponding to the case that the target unmanned aerial vehicle carries the load with different load weights.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a target unmanned aerial vehicle weight prediction method, a target unmanned aerial vehicle weight prediction device and a storage medium.
Background
At present, in plant protection unmanned aerial vehicle application field, because need confirm to acquire the medical kit weight accurately to the medicine spraying amount of accurate control pump head, consequently, need predict target unmanned aerial vehicle weight accurately.
If the situation of falling is avoided, the weight of the target unmanned aerial vehicle needs to be accurately determined. One of the existing common methods for determining the weight of a target unmanned aerial vehicle is as follows: and installing a pressure sensor on the target unmanned aerial vehicle frame, and determining the weight of the load through the pressure sensor. The method is simple and has high precision. However, in the use process, the pressure sensor is deformed, so that the sensor is invalid, and the problem of inaccurate prediction results occurs.
How to realize accurate prediction of the weight of the target unmanned aerial vehicle is a technical problem to be solved.
Disclosure of Invention
Based on this, it is necessary to provide a method, an apparatus, a storage medium, an electronic device and a computer program product for predicting the weight of a target unmanned aerial vehicle, which cannot be realized by the existing methods.
In a first aspect, an embodiment of the present application provides a method for predicting a weight of a target unmanned aerial vehicle, where the method includes:
generating a first weight value of the target unmanned aerial vehicle based on the fitting model;
generating a second weight value of the target unmanned aerial vehicle based on the motor rotating speed corresponding to each horn of the target unmanned aerial vehicle and the rotating speed model;
acquiring the first weight value generated based on the fitting model, the second weight value generated based on the rotating speed model and a weighting coefficient adopted by a prediction model, wherein the prediction model is used for predicting each weight value corresponding to the situation that a target unmanned aerial vehicle carries a load with different load weights;
and inputting the weighting coefficient, the first weight value and the second weight value into the prediction model for prediction, and outputting a corresponding prediction result, wherein the prediction result comprises each prediction weight value corresponding to the condition that the target unmanned aerial vehicle carries the load with different load weights.
In one embodiment, the generating the first weight value of the target drone based on the fitting model includes:
acquiring the current power battery output power of the target unmanned aerial vehicle under the current load weight;
generating the fitting model;
and inputting the output power of the current power battery into the fitting model for fitting processing, and generating the first weight value of the target unmanned aerial vehicle.
In one embodiment, the generating the fitting model includes:
acquiring power battery output power of a target unmanned aerial vehicle corresponding to a plurality of different load weights respectively;
performing curve fitting processing according to the different load weights and the power output power of the power battery of the corresponding target unmanned aerial vehicle, and fitting a corresponding fitting curve;
and generating the fitting model based on the fitting curve.
In one embodiment, the generating the second weight value of the target unmanned aerial vehicle based on the motor rotation speed corresponding to each horn of the target unmanned aerial vehicle and the rotation speed model includes:
acquiring the number of the horn of the target unmanned aerial vehicle, the motor rotating speeds sequentially corresponding to the various horn of the target unmanned aerial vehicle and the tensile force rotating speed curve coefficient;
and sequentially inputting the motor rotating speed and the tensile rotating speed curve coefficient sequentially corresponding to the arms of the target unmanned aerial vehicle into the rotating speed model for estimation processing, and generating the second weight value of the target unmanned aerial vehicle.
In one embodiment, after the outputting the corresponding prediction result, the method further includes:
generating an optimized weighting coefficient;
and carrying out optimization treatment on the prediction model based on the optimized weighting coefficient to obtain an optimized prediction model.
In one embodiment, the generating the optimized weighting coefficients includes:
acquiring each predicted weight value corresponding to the situation that the target unmanned aerial vehicle carries a load with different load weights;
sequentially comparing each predicted weight value with each corresponding actual weight value to obtain a comparison result;
and carrying out optimization processing on the weighting coefficient according to the comparison result, and generating an optimized weighting coefficient.
In one embodiment, after the outputting the corresponding prediction result, the method further includes:
acquiring each predicted weight value corresponding to the situation that the target unmanned aerial vehicle carries a load with different load weights;
comparing each predicted weight value corresponding to the time when the target unmanned aerial vehicle carries the load with different load weights with a preset weight threshold value in sequence to obtain a corresponding comparison result, wherein the preset weight threshold value is a weight threshold value used for judging whether the target unmanned aerial vehicle is in an overload state or not;
and under the condition that the comparison result comprises that any one of the predicted weight values is larger than the preset weight threshold value, a control instruction is sent to the target unmanned aerial vehicle so as to control the target unmanned aerial vehicle to return to a target position based on the control instruction.
In a second aspect, an embodiment of the present application provides a device for predicting a weight of a target unmanned aerial vehicle, where the device includes:
the first generation module is used for generating a first weight value of the target unmanned aerial vehicle based on the fitting model;
the second generation module is used for generating a second weight value of the target unmanned aerial vehicle based on the motor rotating speed corresponding to each horn of the target unmanned aerial vehicle and the rotating speed model;
the obtaining module is used for obtaining the first weight value generated based on the fitting model, the second weight value generated based on the rotating speed model and a weighting coefficient adopted by a prediction model, wherein the prediction model is used for predicting each weight value corresponding to the situation that a target unmanned aerial vehicle carries a load with different load weights;
and the processing module is used for inputting the weighting coefficient, the first weight value and the second weight value into the prediction model for prediction and outputting a corresponding prediction result, wherein the prediction result comprises each prediction weight value corresponding to the case that the target unmanned aerial vehicle carries the load with different load weights.
In a third aspect, embodiments of the present application provide a computer-readable storage medium storing a computer program for performing the above-described method steps.
In a fourth aspect, an embodiment of the present application provides an electronic device, including:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the method steps described above.
In a fifth aspect, embodiments of the present application provide a computer program product comprising a computer program which, when executed by a processor, implements the above-described method steps.
In the embodiment of the application, a first weight value of the target unmanned aerial vehicle is generated based on a fitting model; generating a second weight value of the target unmanned aerial vehicle based on the motor rotating speed corresponding to each horn of the target unmanned aerial vehicle and the rotating speed model; acquiring a first weight value generated based on a fitting model, a second weight value generated based on a rotating speed model and a weighting coefficient adopted by a prediction model, wherein the prediction model is used for predicting each weight value corresponding to the situation that a target unmanned aerial vehicle carries a load with different load weights; and inputting the weighting coefficient, the first weight value and the second weight value into a prediction model for prediction, and outputting a corresponding prediction result, wherein the prediction result comprises each prediction weight value corresponding to the case that the target unmanned aerial vehicle carries the load with different load weights. According to the method for predicting the weight of the target unmanned aerial vehicle, provided by the embodiment of the application, due to the fact that the prediction model capable of predicting each weight value corresponding to the condition that the target unmanned aerial vehicle carries the load with different load weights is introduced, accurate prediction of the weight of the target unmanned aerial vehicle is achieved; in addition, as the sensor is not needed, the risk of sensor failure is effectively reduced, and the cost of the unmanned aerial vehicle is also reduced.
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Exemplary embodiments of the present invention may be more fully understood by reference to the following drawings. The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the application, and not constitute a limitation of the invention. In the drawings, like reference numerals generally refer to like parts or steps.
FIG. 1 is a flowchart of a method for predicting a weight of a target drone according to an exemplary embodiment of the present application;
fig. 2 is a schematic diagram of power battery output power of a corresponding target unmanned aerial vehicle calibrated at different loads in a specific application scenario;
fig. 3 is a schematic diagram of a fitting curve obtained by performing curve fitting processing on the output power of a power battery and different loads of a target unmanned aerial vehicle in a specific application scene;
fig. 4 is a schematic diagram of a correspondence between motor rotation speed and tension data of a motor blade in a specific application scenario;
fig. 5 is a schematic structural diagram of a target unmanned aerial vehicle weight prediction apparatus 500 according to an exemplary embodiment of the present application;
FIG. 6 illustrates a schematic diagram of an electronic device provided in an exemplary embodiment of the present application;
fig. 7 shows a schematic diagram of a computer-readable medium according to an exemplary embodiment of the present application.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It is noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs.
In addition, the terms "first" and "second" etc. are used to distinguish different objects and are not used to describe a particular order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
The embodiments of the application provide a method and a device for predicting weight of a target unmanned aerial vehicle, an electronic device and a computer readable medium, and the method and the device are described below with reference to the accompanying drawings.
Referring to fig. 1, which is a flowchart illustrating a method for predicting a weight of a target unmanned aerial vehicle according to some embodiments of the present application, as shown in fig. 1, the method for predicting a weight of a target unmanned aerial vehicle may include the following steps:
step S101: a first weight value for the target drone is generated based on the fitted model.
In one possible implementation, generating a first weight value for the target drone based on the fitting model includes the steps of:
acquiring the current power battery output power of the target unmanned aerial vehicle under the current load weight;
generating a fitting model;
and inputting the output power of the current power battery into a fitting model for fitting processing, and generating a first weight value of the target unmanned aerial vehicle.
Fig. 2 is a schematic diagram of power battery output power of a corresponding target unmanned aerial vehicle calibrated under different loading conditions in a specific application scenario.
From the schematic diagram shown in fig. 2, it can be known that: aiming at different loads, the power battery output power corresponding to the target unmanned aerial vehicle can be correspondingly output.
The specific steps are as follows:
under the condition of a load of 10Kg, the output power of the corresponding power battery of the corresponding target unmanned aerial vehicle is 0.9KW.
Under the condition of a load of 20Kg, the output power of the corresponding power battery of the corresponding target unmanned aerial vehicle is 2.6KW.
Under the condition of 30Kg of load, the output power of the corresponding power battery of the corresponding target unmanned aerial vehicle is 4.8KW.
Under the condition of 40Kg of load, the output power of the corresponding power battery of the corresponding target unmanned aerial vehicle is 7.8KW.
In one possible implementation, generating the fitting model includes the steps of:
acquiring power battery output power of a target unmanned aerial vehicle corresponding to a plurality of different load weights respectively;
performing curve fitting processing according to the different load weights and the power output power of the power battery of the corresponding target unmanned aerial vehicle, and fitting a corresponding fitting curve;
a fitting model is generated based on the fitting curve.
Fig. 3 is a schematic diagram of power battery output power of a corresponding target unmanned aerial vehicle calibrated under different loading conditions in a specific application scenario.
The fitting model may be generated based on the fitting curve shown in fig. 3, and the fitting formula adopted by the fitting model shown in fig. 3 is shown in the following formula (1):
in the above-mentioned formula (1),
power battery output power for target unmanned aerial vehicle, +.>
Is a first weight value of the target drone.
Step S102: and generating a second weight value of the target unmanned aerial vehicle based on the motor rotating speed corresponding to each horn of the target unmanned aerial vehicle and the rotating speed model.
In one possible implementation manner, the method for generating the second weight value of the target unmanned aerial vehicle based on the motor rotating speed corresponding to each horn of the target unmanned aerial vehicle and the rotating speed model comprises the following steps:
acquiring the number of the horn of the target unmanned aerial vehicle, the motor rotating speeds sequentially corresponding to the various horn of the target unmanned aerial vehicle and the tensile force rotating speed curve coefficient;
and sequentially inputting the motor rotating speed and the tensile rotating speed curve coefficients sequentially corresponding to the arms of the target unmanned aerial vehicle into a rotating speed model for estimation processing, and generating a second weight value of the target unmanned aerial vehicle.
Fig. 4 is a schematic diagram showing a correspondence between motor rotation speed and tension data of a motor blade in a specific application scenario.
In a specific application scenario, the corresponding relation between the motor rotation speed and the tension data is determined through the following formula (2), wherein the formula (2) is specifically shown as follows:
in the above-mentioned formula (2),
the total pulling force of the whole machine of the target unmanned aerial vehicle is +.>
Is a second weight value for the target drone,
for the motor speed of the target unmanned aerial vehicle, < >>
Is the coefficient of the tension rotation speed curve.
The tension rotation speed curve coefficient in the current application scene can be obtained through various data in the schematic diagram shown in fig. 4
。
As shown in fig. 4, a first set of data is read:
505 @ and @ are>
(i.e.: A. I. Is:>
motor speed) is 309.
From the above equation (2), the data shown in fig. 4 can be calculated: coefficient of curve of rotation speed of pulling force
Is 0.005289.
Step S103: acquiring a first weight value generated based on a fitting model, a second weight value generated based on a rotating speed model and a weighting coefficient adopted by a prediction model, wherein the prediction model is used for predicting each weight value corresponding to the situation that a target unmanned aerial vehicle carries a load with different load weights;
step S104: and inputting the weighting coefficient, the first weight value and the second weight value into a prediction model for prediction, and outputting a corresponding prediction result, wherein the prediction result comprises each prediction weight value corresponding to the condition that the target unmanned aerial vehicle carries the load with different load weights.
In an actual application scene, a prediction formula adopted by the prediction model is shown in the following formula (3):
in the above-mentioned formula (3),
each predicted weight value, < >, corresponding to when carrying loads having different load weights for the target unmanned aerial vehicle>
For a first weight value of the target drone, < ->
For the weighting factor>
For a second weight value of the target drone +.>
The value range of (2) is 0, 1.
In an actual application scenario, the prediction model can be constructed through the prediction formula shown in the formula (3), and a mode of constructing the prediction model based on the prediction formula is a conventional mode and is not described herein.
In an actual application scene, through the tension sensor installed on the target unmanned aerial vehicle, each corresponding actual weight value when the target unmanned aerial vehicle carries the load with different load weights can be accurately obtained
. Will->
And the corresponding predicted weight value obtained by the above-mentioned predictive model +.>
Performing alignment (wherein, in the above alignment process, < > is performed>
And->
Corresponding to the same target unmanned aerial vehicle and the same load) to obtain corresponding comparison results; and iterating and optimizing the prediction model according to the comparison result when +.>
Approach->
When the method is used, stopping the iterative process of the prediction model to obtain the optimal weighting coefficient +.>
。/>
In an actual application scene, the weighting coefficient, the first weight value and the second weight value are all input into a prediction model for prediction, and a corresponding prediction result is output, wherein the prediction result comprises each prediction weight value corresponding to the case that the target unmanned aerial vehicle carries a load with different load weights.
Because the above-mentioned predictive model can accurately predict each predictive weight value that corresponds when target unmanned aerial vehicle carries the load thing that has different load weights, consequently, can be applied to the plant protection field with the target unmanned aerial vehicle that adopts above-mentioned predictive model, because the load weight of load thing can be accurately determined, consequently, the spraying volume of the pump head that installs on the plant protection target unmanned aerial vehicle can be accurately controlled.
In addition, because each prediction weight value corresponding to the situation that the target unmanned aerial vehicle carries the load with different load weights can be accurately predicted by the prediction model, if the prediction model predicts that the target unmanned aerial vehicle is in an overload running state, a control instruction is sent to the target unmanned aerial vehicle, so that the target unmanned aerial vehicle is controlled to return to the target position based on the control instruction, the phenomenon that the target unmanned aerial vehicle falls down is avoided, and therefore the flight safety of the target unmanned aerial vehicle is effectively improved.
In a possible implementation manner, after outputting the corresponding prediction result, the method for predicting the weight of the target unmanned aerial vehicle provided in the embodiment of the present application may further include the following steps:
generating an optimized weighting coefficient;
and carrying out optimization treatment on the prediction model based on the optimized weighting coefficient to obtain an optimized prediction model.
In one possible implementation, generating the optimized weighting coefficients includes the steps of:
acquiring each predicted weight value corresponding to the situation that the target unmanned aerial vehicle carries a load with different load weights;
sequentially comparing each predicted weight value with each corresponding actual weight value to obtain a comparison result;
and optimizing the weighting coefficient according to the comparison result to generate an optimized weighting coefficient.
In a possible implementation manner, after outputting the corresponding prediction result, the method for predicting the weight of the target unmanned aerial vehicle provided in the embodiment of the present application may further include the following steps:
acquiring each predicted weight value corresponding to the situation that the target unmanned aerial vehicle carries a load with different load weights;
comparing each predicted weight value corresponding to the target unmanned aerial vehicle carrying the load with different load weights with a preset weight threshold value in sequence to obtain a corresponding comparison result, wherein the preset weight threshold value is a weight threshold value used for judging whether the target unmanned aerial vehicle is in an overload state or not;
and under the condition that the comparison result comprises that any one of the predicted weight values is larger than a preset weight threshold value, sending a control instruction to the target unmanned aerial vehicle so as to control the target unmanned aerial vehicle to return to the target position based on the control instruction.
In an actual application scene, when the target unmanned aerial vehicle enters a weighing mode, the target unmanned aerial vehicle is controlled to be in a hovering state, and the electronic speed regulator sends a prediction result comprising a prediction weight value when the target unmanned aerial vehicle carries a current load to the flight controller. If the predicted result exceeds a preset weight threshold (e.g., the preset weight threshold is set to be equal to or greater than 10 percent of the design weight), a control instruction is sent to the target unmanned aerial vehicle through the flight controller to control the target unmanned aerial vehicle to return to the target position. In addition, the electronic speed regulator is controlled to flash red light through the flight controller so as to prompt a user that the target unmanned aerial vehicle is in an overload running state currently.
In an actual application scenario, the preset weight threshold may be set according to different application scenarios, which is not limited herein.
According to the method for predicting the weight of the target unmanned aerial vehicle, provided by the embodiment of the application, due to the fact that the prediction model capable of predicting each weight value corresponding to the condition that the target unmanned aerial vehicle carries the load with different load weights is introduced, accurate prediction of the weight of the target unmanned aerial vehicle is achieved; in addition, as the sensor is not needed, the risk of sensor failure is effectively reduced, and the cost of the unmanned aerial vehicle is also reduced.
In the foregoing embodiment, a method for predicting the weight of a target unmanned aerial vehicle is provided, and correspondingly, the application further provides a device for predicting the weight of the target unmanned aerial vehicle. The device for predicting the weight of the target unmanned aerial vehicle provided by the embodiment of the application can implement the method for predicting the weight of the target unmanned aerial vehicle, and the device for predicting the weight of the target unmanned aerial vehicle can be realized in a mode of software, hardware or combination of software and hardware. For example, the means for predicting the weight of the target drone may comprise integrated or separate functional modules or units to perform the corresponding steps in the methods described above.
Referring to fig. 5, a schematic diagram of a target unmanned aerial vehicle weight prediction device according to some embodiments of the present application is shown. Since the apparatus embodiments are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points. The device embodiments described below are merely illustrative.
As shown in fig. 5, the target unmanned aerial vehicle weight prediction apparatus 500 may include:
a first generating module 501, configured to generate a first weight value of the target unmanned aerial vehicle based on the fitting model;
the second generating module 502 is configured to generate a second weight value of the target unmanned aerial vehicle based on the motor rotation speed corresponding to each horn of the target unmanned aerial vehicle and the rotation speed model;
an obtaining module 503, configured to obtain a first weight value generated based on a fitting model, a second weight value generated based on a rotational speed model, and a weighting coefficient adopted by a prediction model, where the prediction model is used to predict each weight value corresponding to when the target unmanned aerial vehicle carries a load with different load weights;
the processing module 504 is configured to input the weighting coefficient, the first weight value, and the second weight value into the prediction model to perform prediction, and output a corresponding prediction result, where the prediction result includes each prediction weight value corresponding to when the target unmanned aerial vehicle carries a load with different load weights.
In some implementations of the embodiments of the present application, the first generating module 501 is configured to:
acquiring the current power battery output power of the target unmanned aerial vehicle under the current load weight;
generating a fitting model;
and inputting the output power of the current power battery into a fitting model for fitting processing, and generating a first weight value of the target unmanned aerial vehicle.
In some implementations of the embodiments of the present application, the first generating module 501 is specifically configured to:
acquiring power battery output power of a target unmanned aerial vehicle corresponding to a plurality of different load weights respectively;
performing curve fitting processing according to the different load weights and the power output power of the power battery of the corresponding target unmanned aerial vehicle, and fitting a corresponding fitting curve;
a fitting model is generated based on the fitting curve.
In some implementations of the embodiments of the present application, the second generating module 502 is specifically configured to:
acquiring the number of the horn of the target unmanned aerial vehicle, the motor rotating speeds sequentially corresponding to the various horn of the target unmanned aerial vehicle and the tensile force rotating speed curve coefficient;
and sequentially inputting the motor rotating speed and the tensile rotating speed curve coefficients sequentially corresponding to the arms of the target unmanned aerial vehicle into a rotating speed model for estimation processing, and generating a second weight value of the target unmanned aerial vehicle.
In some implementations of the embodiments of the present application, the predicting device 500 for the target unmanned aerial vehicle weight may further include:
a weighting coefficient optimizing module (not shown in fig. 5) for generating an optimized weighting coefficient after outputting the corresponding prediction result;
the model optimization module (not shown in fig. 5) is configured to perform optimization processing on the prediction model based on the optimized weighting coefficient, so as to obtain an optimized prediction model.
In some implementations of the embodiments of the present application, the weighting coefficient optimization module is specifically configured to:
acquiring each predicted weight value corresponding to the situation that the target unmanned aerial vehicle carries a load with different load weights;
sequentially comparing each predicted weight value with each corresponding actual weight value to obtain a comparison result;
and optimizing the weighting coefficient according to the comparison result to generate an optimized weighting coefficient.
In some implementations of the embodiments of the present application, the acquisition module 503 may be further configured to:
after outputting the corresponding prediction results, acquiring each prediction weight value corresponding to the situation that the target unmanned aerial vehicle carries the load with different load weights;
the target unmanned aerial vehicle weight prediction apparatus 500 may further include:
a comparison module (not shown in fig. 5) for sequentially comparing each predicted weight value corresponding to the target unmanned aerial vehicle carrying the load with different load weights with a preset weight threshold value, so as to obtain a corresponding comparison result, wherein the preset weight threshold value is a weight threshold value for judging whether the target unmanned aerial vehicle is in an overload state;
a sending module (not shown in fig. 5) for sending a control command to the target unmanned aerial vehicle to control the target unmanned aerial vehicle to return to the target location based on the control command, in case the comparison result includes that any one of the respective predicted weight values is greater than a preset weight threshold.
In some implementations of the embodiments of the present application, the predicting device 500 for the weight of the target unmanned aerial vehicle provided by the embodiments of the present application has the same beneficial effects as the predicting method for the weight of the target unmanned aerial vehicle provided by the foregoing embodiments of the present application due to the same inventive concept.
The embodiment of the application also provides an electronic device corresponding to the method for predicting the weight of the target unmanned aerial vehicle provided by the foregoing embodiment, where the electronic device may be an electronic device for a server, for example, a server, including an independent server and a distributed server cluster, so as to execute the method for predicting the weight of the target unmanned aerial vehicle; the electronic device may also be an electronic device for a client, such as a mobile phone, a notebook computer, a tablet computer, a desktop computer, etc., to perform the method for predicting the weight of the target unmanned aerial vehicle.
Referring to fig. 6, a schematic diagram of an electronic device according to some embodiments of the present application is shown. As shown in fig. 6, the electronic device 60 includes: a processor 600, a memory 601, a bus 602 and a communication interface 603, the processor 600, the communication interface 603 and the memory 601 being connected by the bus 602; the memory 601 stores a computer program executable on the processor 600, and the processor 600 executes the method for predicting the weight of the target unmanned aerial vehicle described in the present application when executing the computer program.
The memory 601 may include a high-speed random access memory (RAM: random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. The communication connection between the system network element and at least one other network element is implemented via at least one communication interface 603 (which may be wired or wireless), the internet, a wide area network, a local network, a metropolitan area network, etc. may be used.
The processor 600 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the method for predicting the weight of the target unmanned aerial vehicle may be implemented by an integrated logic circuit of hardware or an instruction in the form of software in the processor 600. The processor 600 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 601 and the processor 600 reads the information in the memory 601 and performs the steps of the method described above in combination with its hardware.
The electronic equipment provided by the embodiment of the application and the method for predicting the weight of the target unmanned aerial vehicle provided by the embodiment of the application are the same in invention conception, and have the same beneficial effects as the method adopted, operated or realized by the electronic equipment.
The present application further provides a computer readable medium corresponding to the method for predicting the weight of the target drone provided in the foregoing embodiment, referring to fig. 7, the computer readable storage medium is shown as an optical disc 70, on which a computer program (i.e. a program product) is stored, where the computer program when executed by a processor performs the method for predicting the weight of the target drone.
It should be noted that examples of the computer readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical or magnetic storage medium, which will not be described in detail herein.
The computer readable storage medium provided by the above embodiment of the present application and the method for predicting the weight of the target unmanned aerial vehicle provided by the embodiment of the present application are the same inventive concept, and have the same advantages as the method adopted, operated or implemented by the application program stored therein.
It is noted that the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, 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 through some communication interface, device or unit indirect coupling or communication connection, 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 each embodiment of the present application 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 such understanding, the technical solution of the present application 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, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the embodiments, and are intended to be included within the scope of the claims and description.
Claims (10)
1. A method for predicting the weight of a target unmanned aerial vehicle, the method comprising:
generating a first weight value of the target unmanned aerial vehicle based on the fitting model;
generating a second weight value of the target unmanned aerial vehicle based on the motor rotating speed corresponding to each horn of the target unmanned aerial vehicle and the rotating speed model;
acquiring the first weight value generated based on the fitting model, the second weight value generated based on the rotating speed model and a weighting coefficient adopted by a prediction model, wherein the prediction model is used for predicting each weight value corresponding to the situation that a target unmanned aerial vehicle carries a load with different load weights;
and inputting the weighting coefficient, the first weight value and the second weight value into the prediction model for prediction, and outputting a corresponding prediction result, wherein the prediction result comprises each prediction weight value corresponding to the condition that the target unmanned aerial vehicle carries the load with different load weights.
2. The method of claim 1, wherein generating the first weight value for the target drone based on the fitting model comprises:
acquiring the current power battery output power of the target unmanned aerial vehicle under the current load weight;
generating the fitting model;
and inputting the output power of the current power battery into the fitting model for fitting processing, and generating the first weight value of the target unmanned aerial vehicle.
3. The method of claim 2, wherein the generating the fitting model comprises:
acquiring power battery output power of a target unmanned aerial vehicle corresponding to a plurality of different load weights respectively;
performing curve fitting processing according to the different load weights and the power output power of the power battery of the corresponding target unmanned aerial vehicle, and fitting a corresponding fitting curve;
and generating the fitting model based on the fitting curve.
4. The method of claim 1, wherein generating the second weight value of the target drone based on the motor speed for each of the drones and the speed model, comprises:
acquiring the number of the horn of the target unmanned aerial vehicle, the motor rotating speeds sequentially corresponding to the various horn of the target unmanned aerial vehicle and the tensile force rotating speed curve coefficient;
and sequentially inputting the motor rotating speed and the tensile rotating speed curve coefficient sequentially corresponding to the arms of the target unmanned aerial vehicle into the rotating speed model for estimation processing, and generating the second weight value of the target unmanned aerial vehicle.
5. The method of claim 1, further comprising, after the outputting the corresponding prediction result:
generating an optimized weighting coefficient;
and carrying out optimization treatment on the prediction model based on the optimized weighting coefficient to obtain an optimized prediction model.
6. The method of claim 5, wherein generating the optimized weighting coefficients comprises:
acquiring each predicted weight value corresponding to the situation that the target unmanned aerial vehicle carries a load with different load weights;
sequentially comparing each predicted weight value with each corresponding actual weight value to obtain a comparison result;
and carrying out optimization processing on the weighting coefficient according to the comparison result, and generating an optimized weighting coefficient.
7. The method of claim 1, further comprising, after the outputting the corresponding prediction result:
acquiring each predicted weight value corresponding to the situation that the target unmanned aerial vehicle carries a load with different load weights;
comparing each predicted weight value corresponding to the time when the target unmanned aerial vehicle carries the load with different load weights with a preset weight threshold value in sequence to obtain a corresponding comparison result, wherein the preset weight threshold value is a weight threshold value used for judging whether the target unmanned aerial vehicle is in an overload state or not;
and under the condition that the comparison result comprises that any one of the predicted weight values is larger than the preset weight threshold value, a control instruction is sent to the target unmanned aerial vehicle so as to control the target unmanned aerial vehicle to return to a target position based on the control instruction.
8. A device for predicting the weight of a target unmanned aerial vehicle, the device comprising:
the first generation module is used for generating a first weight value of the target unmanned aerial vehicle based on the fitting model;
the second generation module is used for generating a second weight value of the target unmanned aerial vehicle based on the motor rotating speed corresponding to each horn of the target unmanned aerial vehicle and the rotating speed model;
the obtaining module is used for obtaining the first weight value generated based on the fitting model, the second weight value generated based on the rotating speed model and a weighting coefficient adopted by a prediction model, wherein the prediction model is used for predicting each weight value corresponding to the situation that a target unmanned aerial vehicle carries a load with different load weights;
and the processing module is used for inputting the weighting coefficient, the first weight value and the second weight value into the prediction model for prediction and outputting a corresponding prediction result, wherein the prediction result comprises each prediction weight value corresponding to the case that the target unmanned aerial vehicle carries the load with different load weights.
9. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program for executing the method of any of the preceding claims 1 to 7.
10. An electronic device, the electronic device comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor being configured to read the executable instructions from the memory and execute the executable instructions to implement the method of any one of the preceding claims 1 to 7.
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