CN116167528A - Unmanned aerial vehicle weight prediction method, unmanned aerial vehicle weight prediction device and storage medium - Google Patents

Unmanned aerial vehicle weight prediction method, unmanned aerial vehicle weight prediction device and storage medium Download PDF

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Publication number
CN116167528A
CN116167528A CN202310453350.2A CN202310453350A CN116167528A CN 116167528 A CN116167528 A CN 116167528A CN 202310453350 A CN202310453350 A CN 202310453350A CN 116167528 A CN116167528 A CN 116167528A
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unmanned aerial
aerial vehicle
target unmanned
weight
weight value
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阳健
庞智
许磊
梁启源
张捷
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Shenzhen Hobbywing Technology Co Ltd
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Shenzhen Hobbywing Technology Co Ltd
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis

Abstract

The application discloses a method, a device and a storage medium for predicting the weight of an unmanned aerial vehicle, wherein the method comprises the following steps: acquiring a plurality of key data for predicting the weight of the target unmanned aerial vehicle, wherein the plurality of key data at least comprise a tension rotation speed curve coefficient of the target unmanned aerial vehicle, motor rotation speeds corresponding to all the arms of the target unmanned aerial vehicle, which are acquired under the condition that the target unmanned aerial vehicle is in a hovering state and in an automatic weighing mode, and a filter coefficient for carrying out filter processing on the preliminary estimated weight value; and inputting the plurality of key data into a prediction model for predicting the weight of the target unmanned aerial vehicle for prediction processing, and outputting a prediction result, wherein the prediction result at least comprises a predicted weight value of the whole target unmanned aerial vehicle.

Description

Unmanned aerial vehicle weight prediction method, unmanned aerial vehicle weight prediction device and storage medium
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a method and a device for predicting weight of an unmanned aerial vehicle and a storage medium.
Background
At present, in the application field of unmanned aerial vehicles, in particular to unmanned aerial vehicles applied to agriculture and forestry plant protection operation, in the actual operation process, because operating personnel can overload and use unmanned aerial vehicles, the unmanned aerial vehicle power sleeve can be caused to be in overload operation. If the unmanned aerial vehicle is seriously overloaded, the phenomenon of falling the unmanned aerial vehicle can occur because the unmanned aerial vehicle cannot be controlled to normally run.
If the situation of falling is avoided, the weight of the unmanned aerial vehicle needs to be accurately acquired. One of the existing common methods for acquiring the weight of the unmanned aerial vehicle is as follows: and installing a pressure sensor on the unmanned aerial vehicle frame, and acquiring 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 detection results occurs.
How to realize the accurate prediction to unmanned aerial vehicle weight is the technical problem who remains to solve.
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 drone, which cannot be realized by the existing methods.
In a first aspect, the present application provides a method for predicting the weight of a drone, the method comprising:
acquiring a plurality of key data for predicting the weight of a target unmanned aerial vehicle, wherein the key data at least comprise a tensile force rotating speed curve coefficient of the target unmanned aerial vehicle, motor rotating speeds corresponding to all the arms of the target unmanned aerial vehicle, which are acquired under the condition that the target unmanned aerial vehicle is in a hovering state and in an automatic weighing mode, and a filter coefficient for performing filter processing on the preliminary estimated weight value;
and inputting the plurality of key data into a prediction model for predicting the weight of the target unmanned aerial vehicle to perform prediction processing, and outputting a prediction result, wherein the prediction result at least comprises a prediction weight value of the whole target unmanned aerial vehicle.
Further comprises:
acquiring a preset weight threshold and the predicted weight value, wherein the preset weight threshold is used for judging whether the target unmanned aerial vehicle is overloaded or not;
comparing the preset weight threshold value with the predicted weight value to obtain a corresponding comparison result;
and determining whether to control the return of the target unmanned aerial vehicle according to the comparison result.
The determining whether to control the target unmanned aerial vehicle to return according to the comparison result comprises the following steps:
and determining that the target unmanned aerial vehicle is in an overload running state in response to the predicted weight value being greater than the preset weight threshold value so as to control the target unmanned aerial vehicle to return.
Before the acquiring the plurality of key data for predicting the weight of the target unmanned aerial vehicle, further comprising:
and generating the tension rotating speed curve coefficient of the target unmanned aerial vehicle.
The generating the tension rotation speed curve coefficient of the target unmanned aerial vehicle comprises the following steps:
acquiring a tension rotating speed curve of the target unmanned aerial vehicle;
sequentially reading each rotating speed data and corresponding pulling force data in the pulling force rotating speed curve;
and generating the tension rotation speed curve coefficient according to each rotation speed data in the tension rotation speed curve and the corresponding tension data.
The step of inputting the plurality of key data into a prediction model for predicting the weight of the target unmanned aerial vehicle for prediction processing, and outputting a prediction result comprises the following steps:
determining the preliminary pre-estimated weight value obtained by preliminary prediction of the weight of the target unmanned aerial vehicle;
acquiring a filter coefficient for performing filter processing on the preliminary estimated weight value;
performing filtering processing on the preliminary estimated weight value according to the filtering coefficient, the preliminary estimated weight value and the previous filtering estimated weight value to obtain a current filtering estimated weight value, wherein the current filtering estimated weight value is a steady filtering estimated weight value;
and taking the current round of filtering pre-estimated weight value as the predicted weight value of the target unmanned aerial vehicle, and outputting the predicted result, wherein the predicted result at least comprises the predicted weight value of the target unmanned aerial vehicle.
The determining a preliminary predicted weight value for preliminary predicting the weight of the target unmanned aerial vehicle comprises:
acquiring the tension rotation speed curve coefficient and the motor rotation speed corresponding to each horn of the target unmanned aerial vehicle, wherein the number of the target unmanned aerial vehicle horns is a positive integer greater than or equal to 4;
and carrying out preliminary prediction on the weight of the target unmanned aerial vehicle according to the tension rotation speed curve coefficient and the motor rotation speed corresponding to each horn of the target unmanned aerial vehicle, and obtaining a corresponding preliminary estimated weight value.
In a second aspect, the present application provides a device for predicting the weight of a drone, the device comprising:
the system comprises an acquisition module, a calculation module and a filtering module, wherein the acquisition module is used for acquiring a plurality of key data for predicting the weight of a target unmanned aerial vehicle, and the plurality of key data at least comprise a tensile force rotating speed curve coefficient of the target unmanned aerial vehicle, a motor rotating speed corresponding to each horn of the target unmanned aerial vehicle which is acquired under the condition that the target unmanned aerial vehicle is in a hovering state and in an automatic weighing mode, and a filtering coefficient for preliminarily predicting a weight value and carrying out filtering processing;
the prediction module is used for inputting the plurality of key data into a prediction model for predicting the weight of the target unmanned aerial vehicle to perform prediction processing and outputting a prediction result, wherein the prediction result at least comprises a prediction weight value of the whole target unmanned aerial vehicle.
In a third aspect, the present application provides a computer readable storage medium storing a computer program for performing the above-described method steps.
In a fourth aspect, 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, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the above-mentioned method steps.
In the method, a plurality of key data for predicting the weight of the target unmanned aerial vehicle are obtained, wherein the key data at least comprise a tensile force rotating speed curve coefficient of the target unmanned aerial vehicle, motor rotating speeds corresponding to all the arms of the target unmanned aerial vehicle, which are obtained when the target unmanned aerial vehicle is in a hovering state and in an automatic weighing mode, and a filter coefficient for performing filter processing on the preliminary predicted weight value; and inputting the plurality of key data into a prediction model for predicting the weight of the target unmanned aerial vehicle for prediction processing, and outputting a prediction result, wherein the prediction result at least comprises a predicted weight value of the whole target unmanned aerial vehicle. According to the method for predicting the weight of the unmanned aerial vehicle, the prediction model capable of predicting the predicted weight value of the whole target unmanned aerial vehicle is introduced, and a plurality of key data are input into the prediction model for predicting the weight of the target unmanned aerial vehicle for prediction processing, wherein the plurality of key data at least comprise the tensile force rotating speed curve coefficient of the target unmanned aerial vehicle, the motor rotating speeds corresponding to the various arms of the target unmanned aerial vehicle, which are obtained when the target unmanned aerial vehicle is in a hovering state and in an automatic weighing mode, and the filter coefficient for performing filter processing on the preliminary predicted weight value, so that a prediction result can be automatically output, and the accurate prediction of the weight of the target unmanned aerial vehicle is realized; 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.
Drawings
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 drone according to an exemplary embodiment of the present application;
FIG. 2 is a schematic diagram of various data of a tension rotation speed curve (for example, a 48 inch blade) in a specific application scenario;
fig. 3 is a schematic structural diagram of a device 300 for predicting the weight of a drone according to an exemplary embodiment of the present application;
FIG. 4 illustrates a schematic diagram of an electronic device provided in an exemplary embodiment of the present application;
fig. 5 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 present application provide a method and apparatus for predicting weight of an unmanned aerial vehicle, an electronic device, and a computer readable medium, and the following description is made with reference to the accompanying drawings.
Referring to fig. 1, which is a flowchart illustrating a method for predicting the weight of a drone according to some embodiments of the present application, as shown in fig. 1, the method for predicting the weight of a drone may include the following steps:
step S101: acquiring a plurality of key data for predicting the weight of the target unmanned aerial vehicle, wherein the plurality of key data at least comprise a tension rotation speed curve coefficient of the target unmanned aerial vehicle, motor rotation speeds corresponding to all the arms of the target unmanned aerial vehicle, which are acquired under the condition that the target unmanned aerial vehicle is in a hovering state and in an automatic weighing mode, and a filter coefficient for carrying out filter processing on the preliminary estimated weight value.
In one possible implementation manner, before acquiring the plurality of key data for predicting the weight of the target unmanned aerial vehicle, the method for predicting the weight of the unmanned aerial vehicle provided in the embodiment of the application further includes the following steps:
and generating a tension rotating speed curve coefficient of the target unmanned aerial vehicle.
In one possible implementation manner, generating a tension rotation speed curve coefficient of the target unmanned aerial vehicle includes the following steps:
acquiring a tension rotating speed curve of the target unmanned aerial vehicle;
sequentially reading each rotating speed data and corresponding pulling force data in the pulling force rotating speed curve;
and generating a tension rotation speed curve coefficient according to each rotation speed data in the tension rotation speed curve and the corresponding tension data.
Fig. 2 is a schematic diagram of various data of a tension rotation speed curve (for example, a 48 inch blade) in a specific application scenario;
the tension rotation speed curve coefficient in the current application scene can be obtained through various data in the schematic diagram shown in fig. 2
Figure SMS_1
。
Figure SMS_2
Formula (1);
in the above-mentioned formula (1),
Figure SMS_3
the total pulling force of the whole machine of the target unmanned aerial vehicle is that: preliminary pre-estimated weight value,/->
Figure SMS_4
For the target unmanned plane->
Figure SMS_5
Motor speed corresponding to the arm, +.>
Figure SMS_6
Representing the number of arms of the target unmanned aerial vehicle, +.>
Figure SMS_7
The method comprises the steps of carrying out a first treatment on the surface of the If the target unmanned aerial vehicle is a rotorcraft, the corresponding number of arms +.>
Figure SMS_8
Is a positive integer greater than or equal to 4, ">
Figure SMS_9
Is the coefficient of the tension rotation speed curve.
As shown in fig. 2, a first set of data is read:
Figure SMS_10
505 @ and @ are>
Figure SMS_11
The motor speed is 309 #, #>
Figure SMS_12
. From the above equation (1), the data shown in fig. 2 can be calculated: tension rotation speed curve coefficient>
Figure SMS_13
Is 0.005289.
Step S102: and inputting the plurality of key data into a prediction model for predicting the weight of the target unmanned aerial vehicle to perform prediction processing, and outputting a prediction result, wherein the prediction result at least comprises a prediction weight value of the whole target unmanned aerial vehicle.
In one possible implementation manner, the method includes the steps of inputting a plurality of key data into a prediction model for predicting the weight of the target unmanned aerial vehicle to perform prediction processing, and outputting a prediction result, wherein the method includes the following steps:
determining a preliminary pre-estimated weight value obtained by preliminary prediction of the weight of the target unmanned aerial vehicle;
acquiring a filter coefficient for performing filter processing on the preliminary estimated weight value;
according to the filter coefficient, the preliminary pre-estimation weight value and the previous-round filter pre-estimation weight value, carrying out filter processing on the preliminary pre-estimation weight value to obtain a current-round filter pre-estimation weight value, wherein the current-round filter pre-estimation weight value is a steady-state filter pre-estimation weight value;
and taking the filter pre-estimated weight value of the current round as the predicted weight value of the whole target unmanned aerial vehicle, and outputting a predicted result, wherein the predicted result at least comprises the predicted weight value of the whole target unmanned aerial vehicle.
In a specific application scenario, the calculation formula for calculating the preliminary pre-estimated weight value may be calculated by using the following formula (1), which is specifically described as follows:
Figure SMS_14
formula (1);
in the above-mentioned formula (1),
Figure SMS_15
the total pulling force of the whole machine of the target unmanned aerial vehicle is that: preliminary pre-estimated weight value,/->
Figure SMS_16
For the target unmanned plane->
Figure SMS_17
The motor rotation speed corresponding to the arm is controlled,/>
Figure SMS_18
representing the number of arms of the target unmanned aerial vehicle, +.>
Figure SMS_19
The method comprises the steps of carrying out a first treatment on the surface of the If the target unmanned aerial vehicle is a rotorcraft, the corresponding number of arms +.>
Figure SMS_20
Is a positive integer greater than or equal to 4, ">
Figure SMS_21
Is the coefficient of the tension rotation speed curve.
Calculating the total pulling force of the whole machine of the target unmanned aerial vehicle, namely: and after the preliminary pre-estimation weight value, carrying out filtering processing on the preliminary pre-estimation weight value according to the filtering coefficient, the preliminary pre-estimation weight value and the previous filtering pre-estimation weight value to obtain a current round of filtering pre-estimation weight value, wherein the current round of filtering pre-estimation weight value is a steady filtering pre-estimation weight value, taking the current round of filtering pre-estimation weight value as a prediction weight value of the whole target unmanned aerial vehicle, and outputting a prediction result, wherein the prediction result at least comprises the prediction weight value of the whole target unmanned aerial vehicle.
In a specific application scenario, the calculation formula for calculating the steady-state filtering pre-estimation weight value may be calculated by using the following formula (2), which is specifically described as follows:
Figure SMS_22
formula (2);
in the above-mentioned formula (2),
Figure SMS_23
the pre-estimated weight values are filtered for this round, namely: predicted weight value of the target unmanned aerial vehicle complete machine; />
Figure SMS_24
Filtering the pre-estimated weight value for the previous round; />
Figure SMS_25
The total pulling force of the whole machine of the target unmanned aerial vehicle is that: the preliminary pre-estimated weight value of this round can be calculated by the calculation formula of the above formula (1), and +.>
Figure SMS_26
Is a filter coefficient.
The above filter coefficients
Figure SMS_27
The calculation can be performed using the following formula (3), as follows:
Figure SMS_28
formula (3);
in the above-mentioned formula (3),
Figure SMS_29
for the filter coefficients +.>
Figure SMS_30
For filtering the cut-off frequency +.>
Figure SMS_31
For controlling the period, by configuring the filter cut-off frequency +.>
Figure SMS_32
The time taken to estimate the weight of the target drone may be set.
In the practical application scenario of the present invention,
Figure SMS_33
typically the smoothing stabilization time is 3.6 +.>
Figure SMS_34
The method comprises the steps of carrying out a first treatment on the surface of the Thus, by configuring the filter cut-off frequency +.>
Figure SMS_35
The time taken to estimate the weight of the target drone may be set.
In one possible implementation, determining a preliminary predicted weight value for preliminary predicting the weight of the target drone includes the steps of:
acquiring a tension rotation speed curve coefficient and motor rotation speeds corresponding to all the arms of the target unmanned aerial vehicle, wherein the number of the target unmanned aerial vehicle arms is a positive integer greater than or equal to 4;
and carrying out preliminary prediction on the weight of the target unmanned aerial vehicle according to the tension rotation speed curve coefficient and the motor rotation speed corresponding to each horn of the target unmanned aerial vehicle, and obtaining a corresponding preliminary estimated weight value.
In a specific application scenario, the calculation formula for calculating the preliminary pre-estimated weight value may be as follows:
Figure SMS_36
formula (1);
in the above-mentioned formula (1),
Figure SMS_37
the total pulling force of the whole machine of the target unmanned aerial vehicle is that: preliminary pre-estimated weight value,/->
Figure SMS_38
For the target unmanned plane->
Figure SMS_39
Motor speed corresponding to the arm, +.>
Figure SMS_40
Representing the number of arms of the target unmanned aerial vehicle, +.>
Figure SMS_41
The method comprises the steps of carrying out a first treatment on the surface of the If the target unmanned aerial vehicle is a rotorcraft, the corresponding number of arms +.>
Figure SMS_42
Is a positive integer greater than or equal to 4, ">
Figure SMS_43
Is the coefficient of the tension rotation speed curve.
In a possible implementation manner, the method for predicting the weight of the unmanned aerial vehicle provided by the embodiment of the application further includes the following steps:
acquiring a preset weight threshold value and a predicted weight value, wherein the preset weight threshold value is used for judging whether the target unmanned aerial vehicle is overloaded or not;
comparing the preset weight threshold value with the predicted weight value to obtain a corresponding comparison result;
and determining whether to control the return of the target unmanned aerial vehicle according to the comparison result.
In one possible implementation manner, determining whether to control the target unmanned aerial vehicle to return according to the comparison result includes the following steps:
in response to the predicted weight value being greater than the preset weight threshold, determining that the target unmanned aerial vehicle is in an overload operation state so as to control the target unmanned aerial vehicle to return; therefore, when the target unmanned aerial vehicle is determined to be in an overload operation state, the target unmanned aerial vehicle is controlled to return to the home as soon as possible, and the situation that the target unmanned aerial vehicle falls down possibly is avoided.
In the 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 unmanned aerial vehicle, the prediction model capable of predicting the predicted weight value of the whole target unmanned aerial vehicle is introduced, and a plurality of key data are input into the prediction model for predicting the weight of the target unmanned aerial vehicle for prediction processing, wherein the plurality of key data at least comprise the tensile force rotating speed curve coefficient of the target unmanned aerial vehicle, the motor rotating speeds corresponding to all the arms of the target unmanned aerial vehicle, which are obtained when the target unmanned aerial vehicle is in a hovering state and in an automatic weighing mode, and the filter coefficient for performing filter processing on the preliminary predicted weight value, so that a prediction result can be automatically output, and the accurate prediction of the weight of the target unmanned aerial vehicle is realized; 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 above embodiment, a method for predicting the weight of an unmanned aerial vehicle is provided, and correspondingly, the application also provides a device for predicting the weight of the unmanned aerial vehicle. The unmanned aerial vehicle weight prediction device provided by the embodiment of the application can implement the unmanned aerial vehicle weight prediction method, and the unmanned aerial vehicle weight prediction device can be realized through software, hardware or a soft-hard combination mode. For example, the unmanned aerial vehicle weight prediction device may include integrated or separate functional modules or units to perform the corresponding steps in the methods described above.
Referring to fig. 3, a schematic diagram of a device for predicting a weight of a drone according to some embodiments of the present disclosure 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. 3, the unmanned aerial vehicle weight prediction apparatus 300 may include:
the obtaining module 301 is configured to obtain a plurality of key data for predicting a weight of the target unmanned aerial vehicle, where the plurality of key data includes at least a tensile rotation speed curve coefficient of the target unmanned aerial vehicle, a motor rotation speed corresponding to each arm of the target unmanned aerial vehicle obtained when the target unmanned aerial vehicle is in a hovering state and in an automatic weighing mode, and a filter coefficient for performing a filter process on the preliminary predicted weight value;
the prediction module 302 is configured to input the plurality of key data into a prediction model for predicting the weight of the target unmanned aerial vehicle to perform prediction processing, and output a prediction result, where the prediction result at least includes a predicted weight value of the complete machine of the target unmanned aerial vehicle.
Acquiring a plurality of key data for predicting the weight of the target unmanned aerial vehicle, wherein the plurality of key data at least comprise a tension rotation speed curve coefficient of the target unmanned aerial vehicle, motor rotation speeds corresponding to all the arms of the target unmanned aerial vehicle, which are acquired under the condition that the target unmanned aerial vehicle is in a hovering state and in an automatic weighing mode, and a filter coefficient for carrying out filter processing on the preliminary estimated weight value;
and inputting the plurality of key data into a prediction model for predicting the weight of the target unmanned aerial vehicle to perform prediction processing, and outputting a prediction result, wherein the prediction result at least comprises a prediction weight value of the whole target unmanned aerial vehicle.
In some implementations of the embodiments of the present application, the acquisition module 301 may also be configured to:
acquiring a preset weight threshold value and a predicted weight value, wherein the preset weight threshold value is used for judging whether the target unmanned aerial vehicle is overloaded or not;
the unmanned aerial vehicle weight prediction apparatus 300 may further include:
a comparison module (not shown in fig. 3) for comparing the preset weight threshold value and the predicted weight value to obtain a corresponding comparison result;
a determining module (not shown in fig. 3) for determining whether to control the target unmanned aerial vehicle to return according to the comparison result.
In some implementations of the embodiments of the present application, the determining module is specifically configured to:
and determining that the target unmanned aerial vehicle is in an overload operation state in response to the predicted weight value being greater than the preset weight threshold value so as to control the target unmanned aerial vehicle to return.
In some implementations of the embodiments of the present application, the apparatus 300 for predicting the weight of the unmanned aerial vehicle may further include:
a generation module (not shown in fig. 3) for generating a pull speed profile coefficient of the target drone before acquiring the plurality of key data for predicting the weight of the target drone.
In some implementations of the embodiments of the present application, the generating module is specifically configured to:
acquiring a tension rotating speed curve of the target unmanned aerial vehicle;
sequentially reading each rotating speed data and corresponding pulling force data in the pulling force rotating speed curve;
and generating a tension rotation speed curve coefficient according to each rotation speed data in the tension rotation speed curve and the corresponding tension data.
In some implementations of the embodiments of the present application, the prediction module 302 is specifically configured to:
determining a preliminary pre-estimated weight value obtained by preliminary prediction of the weight of the target unmanned aerial vehicle;
acquiring a filter coefficient for performing filter processing on the preliminary estimated weight value;
according to the filter coefficient, the preliminary pre-estimation weight value and the previous-round filter pre-estimation weight value, carrying out filter processing on the preliminary pre-estimation weight value to obtain a current-round filter pre-estimation weight value, wherein the current-round filter pre-estimation weight value is a steady-state filter pre-estimation weight value;
and taking the filter pre-estimated weight value of the current round as the predicted weight value of the whole target unmanned aerial vehicle, and outputting a predicted result, wherein the predicted result at least comprises the predicted weight value of the whole target unmanned aerial vehicle.
In some implementations of the embodiments of the present application, the prediction module 302 is specifically configured to:
acquiring a tension rotation speed curve coefficient and motor rotation speeds corresponding to all the arms of the target unmanned aerial vehicle, wherein the number of the target unmanned aerial vehicle arms is a positive integer greater than or equal to 4;
and carrying out preliminary prediction on the weight of the target unmanned aerial vehicle according to the tension rotation speed curve coefficient and the motor rotation speed corresponding to each horn of the target unmanned aerial vehicle, and obtaining a corresponding preliminary estimated weight value.
The apparatus 300 for predicting the weight of the unmanned aerial vehicle provided in the embodiments of the present application in some implementations of the embodiments of the present application has the same beneficial effects as the method for predicting the weight of the unmanned aerial vehicle provided in the foregoing embodiments of the present application because of the same inventive concept.
The embodiment of the application also provides an electronic device corresponding to the method for predicting the weight of the unmanned aerial vehicle provided by the previous embodiment, wherein the electronic device can be an electronic device for a server, such as a server, including an independent server, a distributed server cluster and the like, so as to execute the method for predicting the weight of the 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 above-mentioned method for predicting the weight of the unmanned aerial vehicle.
Referring to fig. 4, a schematic diagram of an electronic device according to some embodiments of the present application is shown. As shown in fig. 4, the electronic device 40 includes: processor 400, memory 401, bus 402 and communication interface 403, processor 400, communication interface 403 and memory 401 being connected by bus 402; the memory 401 stores a computer program executable on the processor 400, and the processor 400 executes the method for predicting the weight of the unmanned aerial vehicle described in the present application when executing the computer program.
The memory 401 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 403 (which may be wired or wireless), the internet, a wide area network, a local network, a metropolitan area network, etc. may be used.
Bus 402 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be divided into address buses, data buses, control buses, etc. The memory 401 is configured to store a program, and the processor 400 executes the program after receiving an execution instruction, and the method for predicting the weight of the unmanned aerial vehicle disclosed in any of the foregoing embodiments of the present application may be applied to the processor 400 or implemented by the processor 400.
The processor 400 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 400 or by instructions in the form of software. The processor 400 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 401, and the processor 400 reads the information in the memory 401, and in combination with its hardware, performs the steps of the above method.
The electronic equipment provided by the embodiment of the application and the unmanned aerial vehicle weight prediction method 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 embodiment also provides a computer readable medium corresponding to the method for predicting the weight of the unmanned aerial vehicle provided in the foregoing embodiment, referring to fig. 5, the computer readable storage medium is shown as an optical disc 50, 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 unmanned aerial vehicle.
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 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 of predicting weight of an unmanned aerial vehicle, the method comprising:
acquiring a plurality of key data for predicting the weight of a target unmanned aerial vehicle, wherein the key data at least comprise a tensile force rotating speed curve coefficient of the target unmanned aerial vehicle, motor rotating speeds corresponding to all the arms of the target unmanned aerial vehicle, which are acquired under the condition that the target unmanned aerial vehicle is in a hovering state and in an automatic weighing mode, and a filter coefficient for performing filter processing on the preliminary estimated weight value;
and inputting the plurality of key data into a prediction model for predicting the weight of the target unmanned aerial vehicle to perform prediction processing, and outputting a prediction result, wherein the prediction result at least comprises a prediction weight value of the whole target unmanned aerial vehicle.
2. The method as recited in claim 1, further comprising:
acquiring a preset weight threshold and the predicted weight value, wherein the preset weight threshold is used for judging whether the target unmanned aerial vehicle is overloaded or not;
comparing the preset weight threshold value with the predicted weight value to obtain a corresponding comparison result;
and determining whether to control the return of the target unmanned aerial vehicle according to the comparison result.
3. The method of claim 2, wherein determining whether to control the target drone for return voyage based on the comparison result comprises:
and determining that the target unmanned aerial vehicle is in an overload running state in response to the predicted weight value being greater than the preset weight threshold value so as to control the target unmanned aerial vehicle to return.
4. The method of claim 1, further comprising, prior to the acquiring the plurality of key data for predicting the weight of the target drone:
and generating the tension rotating speed curve coefficient of the target unmanned aerial vehicle.
5. The method of claim 4, wherein the generating the pull rate profile coefficient for the target drone includes:
acquiring a tension rotating speed curve of the target unmanned aerial vehicle;
sequentially reading each rotating speed data and corresponding pulling force data in the pulling force rotating speed curve;
and generating the tension rotation speed curve coefficient according to each rotation speed data in the tension rotation speed curve and the corresponding tension data.
6. The method according to claim 1, wherein the inputting the plurality of key data into a prediction model for predicting the weight of the target unmanned aerial vehicle for prediction processing, and outputting a prediction result, includes:
determining the preliminary pre-estimated weight value obtained by preliminary prediction of the weight of the target unmanned aerial vehicle;
acquiring a filter coefficient for performing filter processing on the preliminary estimated weight value;
performing filtering processing on the preliminary estimated weight value according to the filtering coefficient, the preliminary estimated weight value and the previous filtering estimated weight value to obtain a current filtering estimated weight value, wherein the current filtering estimated weight value is a steady filtering estimated weight value;
and taking the current round of filtering pre-estimated weight value as the predicted weight value of the target unmanned aerial vehicle, and outputting the predicted result, wherein the predicted result at least comprises the predicted weight value of the target unmanned aerial vehicle.
7. The method of claim 6, wherein determining a preliminary predicted weight value for preliminary predicting the weight of the target drone comprises:
acquiring the tension rotation speed curve coefficient and the motor rotation speed corresponding to each horn of the target unmanned aerial vehicle, wherein the number of the target unmanned aerial vehicle horns is a positive integer greater than or equal to 4;
and carrying out preliminary prediction on the weight of the target unmanned aerial vehicle according to the tension rotation speed curve coefficient and the motor rotation speed corresponding to each horn of the target unmanned aerial vehicle, and obtaining a corresponding preliminary estimated weight value.
8. A device for predicting the weight of an unmanned aerial vehicle, the device comprising:
the system comprises an acquisition module, a calculation module and a filtering module, wherein the acquisition module is used for acquiring a plurality of key data for predicting the weight of a target unmanned aerial vehicle, and the plurality of key data at least comprise a tensile force rotating speed curve coefficient of the target unmanned aerial vehicle, a motor rotating speed corresponding to each horn of the target unmanned aerial vehicle which is acquired under the condition that the target unmanned aerial vehicle is in a hovering state and in an automatic weighing mode, and a filtering coefficient for preliminarily predicting a weight value and carrying out filtering processing;
the prediction module is used for inputting the plurality of key data into a prediction model for predicting the weight of the target unmanned aerial vehicle to perform prediction processing and outputting a prediction result, wherein the prediction result at least comprises a prediction weight value of the whole target unmanned aerial vehicle.
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.
CN202310453350.2A 2023-04-25 2023-04-25 Unmanned aerial vehicle weight prediction method, unmanned aerial vehicle weight prediction device and storage medium Pending CN116167528A (en)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106996794A (en) * 2017-04-20 2017-08-01 佛山科学技术学院 A kind of depopulated helicopter state monitoring apparatus
CN107783421A (en) * 2017-09-30 2018-03-09 深圳禾苗通信科技有限公司 A kind of unmanned plane adaptive quality compensating control method and system
WO2023055878A1 (en) * 2021-09-29 2023-04-06 Wayne State University A smart landing platform with data-driven analytic procedures for unmanned aerial vehicle pre-flight diagnosis

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106996794A (en) * 2017-04-20 2017-08-01 佛山科学技术学院 A kind of depopulated helicopter state monitoring apparatus
CN107783421A (en) * 2017-09-30 2018-03-09 深圳禾苗通信科技有限公司 A kind of unmanned plane adaptive quality compensating control method and system
WO2023055878A1 (en) * 2021-09-29 2023-04-06 Wayne State University A smart landing platform with data-driven analytic procedures for unmanned aerial vehicle pre-flight diagnosis

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Application publication date: 20230526