CN112504320A

CN112504320A - Data validity detection method and device

Info

Publication number: CN112504320A
Application number: CN202011335156.7A
Authority: CN
Inventors: 梁宇恒
Original assignee: Guangzhou Xaircraft Technology Co Ltd
Current assignee: Guangzhou Xaircraft Technology Co Ltd
Priority date: 2017-08-01
Filing date: 2017-08-01
Publication date: 2021-03-16
Anticipated expiration: 2037-08-01
Also published as: CN109323714B; CN109323714A; CN112504320B

Abstract

The embodiment of the invention provides a method and a device for detecting data validity, wherein the method comprises the following steps: when the measurement opportunity is reached, acquiring measurement data measured by a designated sensor in the unmanned aerial vehicle; acquiring flight parameters of the unmanned aerial vehicle; and judging whether the measurement data is valid or not based on the flight parameters. The embodiment of the invention can avoid the influence of invalid data on the measurement precision, and improve the measurement precision, thereby improving the operation quality of the unmanned aerial vehicle.

Description

Data validity detection method and device

The application is a divisional application of patent application with the application number of 201710648651.5 and the application date of 2017/08/01, and the name of the invention is a method and a device for detecting data validity.

Technical Field

The present invention relates to the field of unmanned aerial vehicle technology, and in particular, to a method for data validity detection, a device for data validity detection, an aerial vehicle, and a computer-readable storage medium.

Background

An Unmanned Aerial Vehicle (Unmanned Aerial Vehicle, UAV for short) is an Unmanned Aerial Vehicle. The unmanned aerial vehicle has wide application and is often applied to industries such as plant protection, city management, geology, meteorology, electric power, emergency and disaster relief, video shooting and the like.

In addition to knowing the current altitude of the drone, the height of the drone relative to the ground (i.e., the altitude to the ground) is also known to the drone in order to achieve autonomous flight at low altitudes, particularly near the ground. The altitude information of the unmanned aerial vehicle is generally obtained by measuring through a barometer, a GPS and the like, and the ground altitude can be measured in a sonar ranging mode, a laser ranging mode, a microwave radar ranging mode, a machine vision measuring method and the like. However, since the laser ranging scheme is easily affected by light and has a high price cost, the machine vision measuring method is complex and is also easily affected by light, radar ranging and sonar ranging are not affected by light, and can be used in all weather, and the price cost is relatively low and the system complexity is low. Therefore, radar ranging and sonar ranging are relatively common ranging methods.

The existing radar sensor and sonar sensor are mostly installed on a moving robot or in a fixed space, and the like, so that the data measured by the radar sensor or sonar sensor in the environment is accurate, and complex filtering operation is not needed. However, in unmanned aerial vehicle's application environment, owing to there is the fuselage high-frequency vibration that the high-speed rotation of unmanned aerial vehicle screw arouses, the air current disturbance that the screw rotation arouses, unmanned aerial vehicle is at the quick slope change that relapses of the gesture of flight in-process, comparatively complicated factors such as the power unstability that the screw arouses at high-speed rotation in-process, sonar range finding or the radar range finding that lead to the unmanned aerial vehicle machine to carry introduce comparatively serious noise, can make the range finding failure even, influence the accuracy of measurement, thereby influence the quality of unmanned aerial vehicle operation.

Disclosure of Invention

In view of the above, embodiments of the present invention are proposed in order to provide a method of data validity detection and a corresponding apparatus of data validity detection, an aircraft and a computer-readable storage medium that overcome the above-mentioned problems or at least partially solve the above-mentioned problems.

In order to solve the above problem, an embodiment of the present invention discloses a method for detecting data validity, which is applied to an unmanned aerial vehicle, and the method includes:

when the measurement opportunity is reached, acquiring measurement data measured by a designated sensor in the unmanned aerial vehicle;

acquiring flight parameters of the unmanned aerial vehicle;

and judging whether the measurement data is valid or not based on the flight parameters.

Preferably, the step of determining whether the measurement data is valid based on the flight parameter comprises:

judging whether the measurement data meet preset basic conditions or not;

if not, judging that the measurement data is invalid;

if yes, acquiring a jump variable of the measurement data based on the flight parameters, and judging whether the measurement data is valid or not based on the jump variable.

Preferably, the step of judging whether the measurement data meets a preset basic condition includes:

judging whether the measurement data is larger than a preset design range of the specified sensor;

if so, judging that the measurement data does not meet the basic condition;

if not, acquiring M previous measurement data measured at N previous moments of the specified sensor, and if the measurement data is the same as the M previous measurement data, judging that the measurement data does not meet the basic condition; and if the measured data and the M previous measured data are different, judging that the measured data meet the basic condition, wherein N is greater than 1, and M is greater than 1.

Preferably, the flight parameters include an altitude at which the drone is located; the step of obtaining the jump variable of the measurement data based on the flight parameter comprises:

determining the altitude variation between the altitude of the unmanned aerial vehicle at the current moment and the altitude of the unmanned aerial vehicle at the previous moment;

determining a measurement change value between the measurement data at the current moment and the previous measurement data at the previous moment;

and compensating the altitude variation quantity by the measurement variation value to obtain the jump variable of the measurement data.

Preferably, the designated sensor has a valid counter, and the step of determining whether the measurement data is valid based on the jump variable includes:

if the jumping amount is smaller than or equal to a first preset threshold value, when the measured data at the current moment is judged to be different from the previous measured data at the previous moment, the effective counter is automatically increased by a preset step length;

and when the count in the effective counter is greater than a preset count threshold value, judging that the measurement data is effective.

Preferably, the designated sensors include at least a first sensor and a second sensor, and the measurement data includes first measurement data measured by the first sensor and second measurement data measured by the second sensor; the jump variables comprise a first jump quantity corresponding to first measurement data and a second jump quantity corresponding to second measurement data;

the step of judging whether the measurement data is valid or not based on the jump variable comprises the following steps:

for first measurement data, when the first jump amount is larger than a first preset threshold value, if the second measurement data does not meet the basic condition, judging that the first measurement data is invalid;

and if the second measurement data meets the basic condition, judging whether the first measurement data is valid or not based on the first measurement data and the second measurement data.

Preferably, the step of determining whether the first measurement data is valid based on the first measurement data and the second measurement data includes:

calculating a first difference value of the first measurement data and the second measurement data and a variation trend corresponding to the first difference value;

if the first difference value is smaller than a second preset threshold value and the change trend is smaller than a third preset threshold value, judging that the first measurement data is valid;

and if the first difference is larger than or equal to a second preset threshold value and/or the variation trend is larger than or equal to a third preset threshold value, judging that the first measurement data is invalid.

Preferably, the method further comprises:

and when the first measurement data is judged to be valid due to the fact that the first jump amount is larger than the first preset threshold value, if the previous second measurement data at the previous moment is judged to be invalid due to the fact that the second jump amount is larger than the first preset threshold value, the previous second measurement data is judged to be valid.

Preferably, the method further comprises:

and if the previous measurement data at the previous moment is valid but the measurement data at the current moment is invalid, clearing the valid counter.

The embodiment of the invention also discloses a device for detecting the validity of data, which is applied to an unmanned aerial vehicle, and the device comprises:

the measurement data acquisition module is used for acquiring measurement data measured by a designated sensor in the unmanned aerial vehicle when the measurement opportunity is reached;

the flight parameter acquisition module is used for acquiring flight parameters of the unmanned aerial vehicle;

and the effective judgment module is used for judging whether the measurement data is effective or not based on the flight parameters.

Preferably, the validity judging module includes:

a basic condition judgment submodule for judging whether the measurement data meets a preset basic condition; if not, calling an invalid judgment submodule; if yes, calling a jump quantity judgment submodule;

an invalidity determination submodule for determining that the measurement data is invalid;

and the jump quantity judgment submodule is used for acquiring jump quantity of the measurement data based on the flight parameters and judging whether the measurement data is effective or not based on the jump quantity.

Preferably, the basic condition judgment sub-module includes:

the design range judging unit is used for judging whether the measurement data is larger than the preset design range of the specified sensor or not; if so, judging that the measurement data does not meet the basic condition; if not, calling a continuous data judgment unit;

a continuous data judgment unit, configured to acquire M pieces of previous measurement data measured at N times before the specified sensor, and if the measurement data is the same as the M pieces of previous measurement data, determine that the measurement data does not satisfy the basic condition; and if the measured data is different from the M previous measured data, judging that the measured data meets the basic condition, wherein N is greater than 1, and M is greater than 1.

Preferably, the flight parameters include an altitude at which the drone is located; the jump amount judgment submodule includes:

the altitude variation determining unit is used for determining the altitude variation between the altitude of the unmanned aerial vehicle at the current moment and the altitude of the unmanned aerial vehicle at the previous moment;

a measurement change value determination unit for determining a measurement change value between the measurement data at the current time and the previous measurement data at the previous time;

and the jump variable determining unit is used for compensating the altitude variation quantity by the measurement variation value to obtain the jump variable of the measurement data.

Preferably, the designated sensor has a valid counter, and the jump amount determination submodule includes:

a counter self-increment unit, configured to, if the jump amount is smaller than or equal to a first preset threshold, self-increment the effective counter by a preset step length when it is determined that measurement data at a current time is different from previous measurement data at a previous time;

and the validity judging unit is used for judging that the measurement data is valid when the count in the validity counter is greater than a preset count threshold value.

the jump amount judgment submodule includes:

an invalidity determining unit, configured to determine, for first measurement data, that the first measurement data is invalid if the second measurement data does not satisfy the basic condition when the first transition amount is greater than a first preset threshold;

and the validity judging unit is used for judging whether the first measurement data is valid or not based on the first measurement data and the second measurement data if the second measurement data meets the basic condition.

Preferably, the validity judging unit includes:

the difference value calculating subunit is used for calculating a first difference value between the first measurement data and the second measurement data and a variation trend corresponding to the first difference value;

the validity judging subunit is configured to judge that the first measurement data is valid if the first difference is smaller than a second preset threshold and the variation trend is smaller than a third preset threshold;

and the invalidity judging subunit is used for judging that the first measurement data is invalid if the first difference is greater than or equal to a second preset threshold and/or the change trend is greater than or equal to a third preset threshold.

Preferably, the apparatus further comprises:

and the judging module is used for judging that the prior second measurement data is valid if the prior second measurement data at the previous moment is judged to be invalid due to the fact that the second jumping amount is larger than the first preset threshold value when the first measurement data is judged to be valid due to the fact that the first jumping amount is larger than the first preset threshold value.

Preferably, the apparatus further comprises:

and the counter zero clearing module is used for clearing the effective counter if the previous measured data at the previous moment is effective but the measured data at the current moment is invalid.

The embodiment of the invention also discloses an aircraft, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the steps of the method when executing the program.

The embodiment of the invention also discloses a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program realizes the steps of the method when being executed by a processor.

The embodiment of the invention has the following advantages:

in the embodiment of the invention, when the measurement opportunity is reached, the flight controller of the unmanned aerial vehicle can acquire the measurement data measured by the designated sensor in the unmanned aerial vehicle, and the effectiveness judgment can be carried out on the measurement data by combining the acquired real-time flight parameters of the unmanned aerial vehicle, so that the influence of invalid data on the measurement precision is avoided, the measurement precision is improved, and the operation quality of the unmanned aerial vehicle is improved.

Drawings

FIG. 1 is a flowchart illustrating a first embodiment of a method for data validity checking according to the present invention;

FIG. 2 is a flowchart illustrating steps of a second embodiment of a method for data validity detection according to the present invention;

FIG. 3 is a flow chart of the steps of one type of data filtering of the present invention;

FIG. 4 is a flow chart of the data fusion steps of the present invention;

fig. 5 is a block diagram of an apparatus for data validity detection according to an embodiment of the present invention.

Detailed Description

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.

In the unmanned aerial vehicle plant protection operation process, can control unmanned aerial vehicle through flight control system (fly control for short) and accomplish whole flight processes such as take-off, air flight, execution operation task and return journey, fly to control to unmanned aerial vehicle be equivalent to the driver to there being the effect of man-machine, be one of unmanned aerial vehicle most core technique.

This flight control system can include the ground satellite station, and the ground satellite station can communicate with unmanned aerial vehicle through communication module, and in the realization, this ground satellite station can be for handing the ground satellite station, and wherein can embed high accuracy GPS, support the quick survey and drawing on irregular block boundary, when using this ground satellite station, need not to connect the computer, can directly adjust unmanned aerial vehicle flight parameter. The ground station has an intelligent air route planning function, supports the presetting of a spray point switch, and can effectively avoid the phenomenon of heavy spray or missed spray in the operation process. In the spraying process, the user can also fly and spray the state through the real-time supervision of ground satellite station, let spray more accurate, high-efficient.

A sensor for distance measurement can be installed in the unmanned aerial vehicle, so that the unmanned aerial vehicle can acquire the height of the unmanned aerial vehicle in real time. According to the embodiment of the invention, the measurement data can be obtained through the designated sensor in the unmanned aerial vehicle, and the validity of the measurement data is judged, so that the influence of invalid data on the measurement precision is avoided, the measurement precision is improved, and the operation quality of the unmanned aerial vehicle is improved.

Referring to fig. 1, a flowchart of a first step of a first method embodiment of data validity detection of the present invention is shown, where the method may be applied to an unmanned aerial vehicle, and the embodiment of the present invention may include the following steps:

step 101, when a measurement opportunity is reached, acquiring measurement data measured by a specified sensor in the unmanned aerial vehicle;

102, acquiring flight parameters of the unmanned aerial vehicle;

and 103, judging whether the measurement data is valid or not based on the flight parameters.

Referring to fig. 2, a flowchart of steps of a second embodiment of the data validity detection method according to the present invention is shown, where the method may be applied to an unmanned aerial vehicle, and the embodiment of the present invention may include the following steps:

step 201, when a measurement opportunity is reached, acquiring measurement data measured by a specified sensor in the unmanned aerial vehicle;

in an embodiment of the present invention, the designated sensor may include at least one of a sonar sensor, a radar sensor, and the like.

The sonar sensor sends out a sound wave signal, can reflect back when meeting object, can calculate unmanned aerial vehicle distance and position from the object according to reflection time and wave pattern.

The radar sensor can sense the existence, the moving speed, the static distance, the angle of the object and the like by transmitting and receiving microwaves.

This measured data can be the unmanned aerial vehicle's that the sensor measurement obtained to ground distance value.

In practice, if the designated sensor includes a first sensor and a second sensor, the corresponding measurement data may include first measurement data measured by the first sensor and second measurement data measured by the second sensor.

For example, when the first sensor is a sonar sensor, the first measurement data may be a ground distance value hs of the drone measured by the sonar sensor. When the second sensor is a radar sensor, the second measurement data may be a ground distance value hr of the drone measured by the radar sensor, and the first measurement data may be represented by hs, and the second measurement data may be represented by hr.

In a specific implementation, the measurement interval and the measurement lifecycle may be set in advance by the flight controller, for example, 20 measurements per second are set, and then the measurement timing may be determined according to the time interval, for example, the measurement timing may be 1/20s, 1/10s, 3/20s, … s, 1s, respectively.

Step 202, acquiring flight parameters of the unmanned aerial vehicle;

in a specific implementation, the flight controller may further obtain real-time flight parameters of the drone, and as an example, the flight parameters may include, but are not limited to: the real-time altitude, horizontal flight speed, flight course angle and the like of the unmanned aerial vehicle.

Step 203, judging whether the measurement data meet preset basic conditions or not; if not, go to step 204; if yes, go to step 205;

step 204, judging that the measurement data is invalid;

the embodiment of the invention can preset basic conditions as a first judgment program for effectiveness judgment.

In a preferred embodiment of the present invention, step 203 further comprises the following sub-steps:

a substep S11 of determining whether the measurement data is greater than a preset design range of the designated sensor; if yes, performing the substep S12, otherwise, performing the substep S13;

a substep S12 of determining that the measurement data does not satisfy the basic condition;

for example, if the preset design range of the sensor is 4 meters and the echo of the measurement data is 5 meters, the measurement data exceeds the design range, and at this time, it may be determined that the measurement data does not satisfy the basic condition, and it may be determined that the measurement data is invalid.

A substep S13, acquiring M previous measurement data measured N times before the designated sensor, and if the measurement data is the same as the M previous measurement data, determining that the measurement data does not satisfy the basic condition; and if the measured data and the M previous measured data are different, judging that the measured data meet the basic condition, wherein N is greater than 1, and M is greater than 1.

If the measured data does not exceed the design range of the specified sensor, then substep S13 is continued.

In specific implementation, since the ground distance of the unmanned aerial vehicle in the flight process can be changed or fluctuated, if M data measured in a period of time are equal, the measured data measured by the sensor does not satisfy the basic condition, and then the measured data can be judged to be invalid.

In practice, N, M may be an appropriate value selected according to the measurement accuracy of the sensor, the measurement frequency, and the moving speed of the drone carrier, and in practice, N and M may have the same value, for example, N, M may have a value of 5.

If the measurement data measured at the current time and the previous M previous measurement data are different, it may be determined that the measurement data at the current time satisfies the basic condition (i.e., the measurement data does not exceed the preset design range and is different from the previous M previous measurement data), and at this time, step 205 may be continuously performed.

Step 205, acquiring a jump variable of the measurement data based on the flight parameter, and judging whether the measurement data is valid based on the jump variable.

In a preferred embodiment of the present invention, the step of obtaining the jump variable of the measurement data based on the flight parameter may include the following sub-steps:

a substep S21 of determining an altitude variation between the altitude of the drone at the current time and the altitude at the previous time;

a substep S22 of determining a measurement variation value between the measurement data at the current time and the previous measurement data at the previous time;

and a substep S23, compensating the altitude variation by the measurement variation value to obtain a jump variable of the measurement data.

In this embodiment of the present invention, the jump amount may include a first jump variable corresponding to the first measurement data, and a second jump variable corresponding to the second measurement data.

For example, for hr, the corresponding measurement variation dr ═ hr-hr _ o |, where hr is the measurement data at the current time and hr _ o is the previous measurement data at the previous time.

And a second jump variable dr1 corresponding to hr is | hr-hr _ o + hg _ o-hg |, where hg _ o-hg is an altitude variation calculated according to the altitude of the unmanned aerial vehicle at the current time and the altitude at the previous time.

For hs, the corresponding measurement variation ds ═ hs-hs _ o |, where hs is the measurement data at the current time and hs _ o is the previous measurement data at the previous time.

The first hop variable ds1 for hs is ═ hs-hs _ o + hg _ o-hg |.

It should be noted that when the altitude precision is poor or there is no altitude data, dr, ds can be used to replace dr1, ds1, i.e. no compensation for the altitude variation of the carrier is performed.

In a specific implementation, N1 and T1 may select an appropriate value according to the measurement frequency of the sensor and the moving speed of the drone, for example, N1 may be 10, and T1 may be 0.3 m.

In a preferred embodiment of the present invention, the designated sensor has a corresponding valid counter, and step 205 may further comprise the sub-steps of:

in the sub-step S31, if the jump amount is smaller than or equal to a first preset threshold, when it is determined that the measurement data at the current time is different from the previous measurement data at the previous time, the effective counter is increased by a preset step length;

and a substep S32, determining that the measurement data is valid when the count in the valid counter is greater than a preset count threshold.

For example, if ds1< ═ T2, when hs! When hs _ o, the corresponding valid counter Tc _ s + +, and when Tc _ s > the preset count threshold, hs is determined to be valid, at this time, the state of hs at the current time may be set to a valid state, i.e., True _ s is 1.

In a specific implementation, the first preset threshold T2 may be set according to specific characteristics of the sensor, for example, the first preset threshold T2 is set to 1 m.

The fourth preset threshold may be a value according to a measurement frequency of the sensor and a characteristic of the actual sensor, for example, the fourth preset threshold may be 3.

In a preferred embodiment of the present invention, when the designated sensors include at least a first sensor and a second sensor, step 205 may further include the sub-steps of:

substep S41, for a first measurement data, when the first jump amount is greater than a first preset threshold, if the second measurement data does not satisfy the basic condition, determining that the first measurement data is invalid;

for example, if the sensor currently performing validity determination is a sonar sensor, if ds1 is greater than T2, and if data hr measured by the radar sensor is invalid due to exceeding the design range of the radar sensor or if M consecutive data are equal (that is, hr does not satisfy the basic condition), it may be determined that hs at the current time is also invalid.

And a substep S42 of determining whether the first measurement data is valid based on the first measurement data and the second measurement data if the second measurement data satisfies the basic condition.

For example, if hr satisfies the base condition, it can be determined whether hs is valid based on hr and hs.

In a preferred embodiment of the present invention, the sub-step S42 further includes the following sub-steps:

substep S421, calculating a first difference between the first measurement data and the second measurement data, and a variation trend corresponding to the first difference;

substep S422, if the first difference is smaller than a second preset threshold and the variation trend is smaller than a third preset threshold, determining that the first measurement data is valid;

in the sub-step S423, if the first difference is greater than or equal to a second preset threshold and/or the variation trend is greater than or equal to a third preset threshold, it is determined that the first measurement data is invalid.

Specifically, when ds1 is greater than T2, if data hr measured by the radar sensor is not invalid due to exceeding the design range of the radar sensor or the fact that M consecutive data are equal, a first difference between hr and hs at the current time and a variation trend corresponding to the first difference can be calculated.

In one embodiment, the first difference drs ═ hr-hs |, which represents the difference in measured distances of the two sensors.

The variation trend ddrs corresponding to the first difference is | hr-hs | - | hr _ o-hs _ o |, i.e., the current distance difference minus the distance difference at the previous time.

If drs is smaller than a second preset threshold and ddrs is smaller than a third preset threshold, the first measurement data at the current moment can be judged to be valid; otherwise, if drs is greater than or equal to the second preset threshold and/or ddrs is greater than or equal to the third preset threshold, it may be determined that the first measurement data at the current time is invalid.

In a specific implementation, the second preset threshold may be set to T3, which may be set according to specific characteristics of the sensor, typically the threshold T2>2 × threshold T3, for example, the second preset threshold T3 is set to 0.4 m.

The third threshold value may be set to a value of 0.

In a preferred embodiment of the present invention, the method may further include the following steps:

For example, if the measurement data at the previous time is valid, that is, True _ s is 1, and the measurement data at the current time is invalid, that is, True _ s is 0, the corresponding valid counter Tc _ s is cleared, that is, Tc _ s is 0.

In the embodiment of the present invention, the data validity judgment of one sensor in the unmanned aerial vehicle may also affect the data validity judgment result of another sensor, and then the embodiment of the present invention may further include the following steps:

For example, if True _ s is 1, if the hr _ o state at the previous time is invalid and the second transition amount corresponding to the previous time or before is greater than the first preset threshold, it is determined as invalid, that is, since dr1 is greater than T2, drs is greater than or equal to the second preset threshold and/or ddrs is greater than or equal to the third preset threshold, which results in invalidity, and the result of hr _ o is determined as valid.

In the embodiment of the invention, the validity of the measurement data measured by the designated sensor can be judged based on the flight parameters of the unmanned aerial vehicle, so that the influence of invalid data on the measurement precision can be avoided, the measurement precision is improved, and the operation quality of the unmanned aerial vehicle is improved.

In a preferred embodiment of the present invention, after the validity determination is performed on the measurement data, the initial ground distance may be determined based on the valid measurement data.

In a specific implementation, the number of valid measurement data therein, and the hop count of the valid measurement data, may be determined for the latest N1 time instants of measurement data obtained. If the measured data of the N1 moments are not invalid at the same time, the number of valid measured data is not less than N1, and the jump variables of the valid measured data are all less than the threshold T1, the invalid measured data can be eliminated, and the rest valid measured data are added and averaged to obtain the initial ground distance.

In the embodiment of the present invention, a data filtering process may be further included

Referring to fig. 3, a flow chart of steps of data filtering is shown, which may include the steps of:

step 301, determining a filter coefficient corresponding to the measured data based on the flight parameter;

in an embodiment of the invention, the filter coefficients may be determined based on flight parameters.

In a preferred embodiment of the present invention, step 301 further comprises the following sub-steps:

step 301-1, determining a first attenuation parameter corresponding to the measurement data based on the flight parameter;

in a preferred embodiment of the present invention, step 301-1 may comprise the following sub-steps:

301-1-1, acquiring a measurement difference value between the measurement data of the unmanned aerial vehicle at the current moment and the target measurement data at the previous moment;

specifically, if one sensor is designated, the measurement difference is a measurement difference between the measurement data at the current time and the target measurement data at the previous time.

For example, the measurement difference ds2 corresponding to hs is | hs-hsf |, and the measurement difference dr2 corresponding to hr is | hr-hrf |, where hsf and hrf are target measurement data obtained by filtering the measurement data at the previous time.

In practice, if more than two sensors are specified, the measurement difference may be a measurement difference between the measurement data at the current time and the data subjected to the fusion processing at the previous time.

For example, the measurement difference ds4 corresponding to hs is | hs-hout |, and the measurement difference dr4 corresponding to hr is | hr-hout |, where hout is the data obtained by filtering and fusing the measurement data at the previous time.

Step 301-1-2, when the attenuation parameter corresponding to the previous time is smaller than the jump variable corresponding to the measurement data, if the jump quantity is smaller than or equal to a preset jump quantity threshold value, setting the attenuation parameter as the jump variable as a first attenuation parameter; if the jumping amount is larger than a preset jumping amount threshold value, setting the first attenuation parameter as the jumping amount adjusted according to the change trend;

if the measured data is hs, the corresponding first attenuation parameter can be represented as Epfs, if the Epfs at the previous moment is less than the jump variable ds1, it is determined whether the measured data hr measured by another sensor is valid and ds1 is large (e.g., greater than a preset jump threshold fds1), if hr is valid and ds1 is greater than fds1, then Epfs is calculated as ds1+ ddrs, and if hr is invalid or jump variable dr 1< fds1, then Epfs is calculated as ds 1.

In a specific implementation, fds1 can be set according to actual conditions such as the measurement frequency and the measurement accuracy of the sensor, for example, fds1 can be set to 0.5 m.

After obtaining the Epfs, the Epfs may be limited, and in one embodiment, one of the limiting processing modes is as follows: if Epfs > threshold EP1, then Epfs ═ EP 1; if Epfs < value 0, then Epfs is a value of 0.

In a specific implementation, EP1 may be set according to the actual situation of the characteristics of the measurement frequency and the measurement accuracy of the sensor, for example, EP1 may be set to 1 m.

In practice, the designated sensor further has a corresponding jump counter Cpfs, and after limiting Epfs, the jump counter may be cleared, that is, Cpfs is equal to 0.

301-1-3, when the attenuation parameter corresponding to the previous moment is greater than or equal to the jump quantity, if the jump quantity is greater than a preset jump quantity threshold, clearing the jump counter, and if the jump quantity is less than or equal to the preset jump quantity threshold, self-increasing the jump counter by a first preset step length; if the count in the jump counter is larger than a preset delay coefficient threshold value, attenuation calculation is carried out on the attenuation parameter corresponding to the previous moment according to a preset attenuation factor to obtain a first attenuation parameter;

specifically, if Epfs at the previous time is greater than or equal to the jump variable ds1, if ds1 > fds1, the jump counter is cleared, i.e., Cpfs is 0, and if ds1< > fds1, the jump counter is self-incremented by a first predetermined step size, i.e., Cpfs + +.

And if the Cpfs is larger than the preset delay coefficient threshold J _ R, performing attenuation calculation on the attenuation parameter corresponding to the previous moment according to a preset attenuation factor to obtain a first attenuation parameter. For example, Epfs ═ Epfs × SJR, where SJR is the attenuation factor.

In the concrete implementation, the larger the SJR is, the slower the attenuation speed is, the larger the Epfs is, the stronger the corresponding filtering is, the SJR can be set according to the actual filtering bandwidth, and the set value range can be as follows: 0< SJR <1, for example, SJR may be set to 0.8.

J _ R can be set according to actual conditions such as the measurement frequency and the measurement accuracy of the sensor, for example, J _ R is set to 5.

If Cpfs < J _ R, then Epfs is kept constant, i.e. Epfs ═ Epfs.

Step 301-1-5, if the first attenuation parameter is greater than the measurement difference, setting the first attenuation parameter as the measurement difference.

After determining the first attenuation parameter, the first attenuation parameter Epfs may be compared with the above measurement difference ds2 or ds4, and if Epfs is greater than ds2 or ds4, it indicates that the accuracy of the current measurement result is high, and in this case, in order to speed up the convergence condition, Epfs may be made equal to ds2 or ds 4.

301-2, carrying out normalization transformation on the first attenuation parameter to obtain a second attenuation parameter;

after the first attenuation parameter is finally determined, a normalization transformation may be performed on the first attenuation parameter to obtain a normalized second attenuation parameter Kpfs, wherein the normalization transforms the data Epfs from 0 to EP1 to the 0 to 1 region.

In one embodiment, a process for normalizing a transform is as follows:

if Epfs < threshold EP2, Kpffs is 0; if not, then,

if Epfs is > -EP2, Kpfs is 1/(EP1-EP2) × Epfs-EP 2/(EP1-EP 2).

In a specific implementation, EP2 may be set according to the actual situation of the characteristics of the measurement frequency and the measurement accuracy of the sensor, for example, EP2 may be set to 0.1 m.

Step 301-3, determining a filter coefficient based on the second attenuation parameter, wherein the filter coefficient increases with decreasing second attenuation parameter.

For example, after determining the second attenuation parameter Kpfs for the initial measurement data hs obtained by the sonar sensor, the filter coefficient KS may be calculated from Kpfs, and it may be set such that the larger Kpfs, the smaller KS.

In one embodiment, KS may be calculated using a non-linear transformation method, e.g.,

KS＝1.0-Kpfs*Kpfs。

in the embodiment of the present invention, after determining the filter coefficient, the filter coefficient may be further subjected to a limiting process, for example, for KS, one limiting process method is as follows:

if KS > threshold K1, KS ═ K1;

if KS < threshold K2, KS is K2.

Among them, the thresholds K1 and K2 may be set according to the actual low-pass filtering bandwidth requirement, for example, K1-0.5 and K2-0.05 may be set.

And step 302, filtering the measurement data by using the filter coefficient to obtain target measurement data.

In one embodiment, the filtering process may include a low-pass filtering process, for example, for hs, the formula of low-pass filtering calculation according to the filter coefficient to obtain the target measurement data is as follows:

hsf＝hsf+KS*(hs-hsf)。

it should be noted that, besides the low-pass filtering method, other filtering methods may be used to perform filtering processing, for example, median filtering, kalman filtering, and the embodiment of the present invention is not limited to this.

In the embodiment of the present invention, if the designated sensors at least include the first sensor and the second sensor, the first measurement data hs corresponds to the first target measurement data hsf, and the second measurement data hr corresponds to the second target measurement data hrf according to the filtering process.

The embodiment of the present invention may further include the following steps: and correcting the first target measurement data and/or the second target measurement data.

For example, if hsf is greater than both hr and hs, let hsf equal the larger of the two data, hr and hs; if hsf is less than both hr and hs, let hsf equal the smaller of the two data, hr and hs.

Similarly, if hrf is greater than both hr and hs, let hrf equal the larger of the two data, hr and hs; if hrf is less than both hr and hs, let hrf equal the smaller of the two data, hr and hs.

In a preferred embodiment of the present invention, when the first measurement data and the second measurement data are both valid, the embodiment of the present invention may further include a data fusion process, and referring to a flow chart of data fusion steps shown in fig. 4, the data fusion process may include the following steps:

step 401, determining a first weight value corresponding to the first measurement data and a second weight value corresponding to the second measurement data based on the flight parameter;

in a preferred embodiment of the present invention, step 401 may further include the following sub-steps:

step 401-1, calculating a difference between the first target measurement data and the second target measurement data, and recording the difference as a target data difference;

for example, the target data difference drfsf ═ hrf-hsf |.

Step 401-2, if the target data difference is smaller than a preset filter difference threshold, calculating a first sum of a first filter coefficient and a second filter coefficient, setting a first weight value as a proportion of the first filter coefficient in the first sum, and setting a second weight value as a proportion of the second filter coefficient in the first sum;

in a specific implementation, if drfsf is less than the preset filtering difference threshold drsf, which indicates that the filtering results of the two sensors are very close to each other, at this time, a nonlinear polynomial may be used to calculate the first weight value ksh and the second weight value krh based on KS (a first filtering coefficient, i.e., a filtering coefficient of a first designated sensor) and KR (a second filtering coefficient, i.e., a filtering coefficient of a second designated sensor).

In one embodiment, ksh ═ KS/(KS ═ KS + KR ═ KR); krh KR/(KS + KR).

In a specific implementation, drsf may be set according to the measurement accuracy of the sensor and the actual use environment, and for example, drsf may be set to 0.3 m.

Step 401-3, if the target data difference is greater than or equal to the preset filtering difference threshold, calculating a second difference between the fused measured data at the first time and the first target measured data at the current time, and calculating a third difference between the fused measured data at the first time and the second target measured data at the current time, and calculating a second sum of the second difference and the third difference, setting the first weight value as a ratio of the second difference to the second sum, and setting the second weight value as a ratio of the third difference to the second sum.

In a specific implementation, if drfsf > — a preset filtering difference threshold drsf, which indicates that the filtering results of the two sensors are relatively different, a second difference ds3 between the fused measurement data at the previous time and the target measurement data of the first specified sensor at the current time and a third difference dr3 between the fused measurement data at the previous time and the target measurement data of the second specified sensor at the current time may be calculated.

That is, ds3 ═ Hout-hsf |, dr3 ═ Hout-hrf |.

Then, based on ds3 and dr3, a nonlinear polynomial may be employed to calculate the first weight value ksh and the second weight value krh.

In one embodiment, ksh ═ ds3 ═ ds3/(dr3 × dr3+ ds3 ×) ds 3); krh dr3 dr3/(dr3 dr3+ ds3 ds 3).

Step 402, performing fusion processing on the first measurement data and the second measurement data by using the first weight value and the second weight value to obtain fusion measurement data.

In a preferred embodiment of the present invention, step 402 may comprise the following sub-steps:

step 402-1, determining a correction factor corresponding to the flying speed at the current moment;

in a particular implementation, the greater the speed of movement, the smaller the correction factor.

For example, when the moving speed is greater than or equal to 0.5m/s, the correction factor K _ O is 0.5; otherwise, when the moving speed is less than 0.5m/s, the correction factor K _ O is 0.8.

Step 402-2, performing weighting operation on the first target measurement data and the second target measurement data based on the first weight value and the second weight value to obtain calibration measurement data;

specifically, the calibration measurement data is krh hrf + ksh hsf.

And 402-3, performing fusion calculation on the calibration measurement data and the fusion measurement data at the previous moment based on the correction factor to obtain the fusion measurement data at the current moment.

In one embodiment, the fused measurement data Hout ═ K _ O ═ Hout + (1.0f-K _ O) (krh ═ hrf + ksh × hsf).

It should be noted that the above fusion formula may also adopt a variance weight fusion mode, which is not limited in the embodiment of the present invention.

In another embodiment of the present invention, when hr is enabled and hs is disabled, krh may be made 1.0f and ksh may be made 0.0f, and the following formula may be used to perform the fusion calculation:

Hout＝K_O*Hout+(1.0f-K_O)*(krh*hrf+ksh*hsf)。

in this case, the sonar filter may be corrected such that KS is equal to a small fixed value (for example, KS is set to 0.1), and the formula for calculating the sonar filter for correction is as follows: hsf ═ hsf + KS — (Hout-hsf).

In another embodiment of the present invention, when hr is invalid and hs is valid, ksh may be 1.0f and krh may be 0.0f, and the following formula may be used to perform the fusion calculation:

Hout＝K_O*Hout+(1.0f-K_O)*(krh*hrf+ksh*hsf)。

in this case, the radar filter may be corrected such that KR is equal to a small fixed value (for example, KR is set to 0.1), and the formula for performing the correction calculation on the radar filter is as follows: hrf + KR (Hout-hrf).

And when hr is invalid and hs is also invalid, maintaining the data at the previous moment, giving a judgment result that the data at the current moment are invalid, and re-performing the initialization process if the data of the two sensors are invalid at the same time for a long time.

In the embodiment of the invention, the characteristics of continuous change of the altitude and the lifting speed of the unmanned aerial vehicle in the moving process are combined, the effectiveness judgment, the filtering and the fusion are carried out on the measurement data of two or more appointed sensors in the unmanned aerial vehicle, the fused measurement data are obtained, the noise and the measurement error of the measurement data of the two or more appointed sensors in the unmanned aerial vehicle environment can be effectively filtered, the filtering and fusion effect is good, the phase delay is small, the response is sensitive, and the accuracy and the stability of the measurement of the ground clearance data of the unmanned aerial vehicle are improved.

It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the illustrated order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.

Referring to fig. 5, a block diagram of an embodiment of the apparatus for detecting data validity in the present invention is shown, and the apparatus is applied to an unmanned aerial vehicle, and may include the following modules:

a measurement data obtaining module 501, configured to obtain measurement data measured by a designated sensor in the unmanned aerial vehicle when a measurement opportunity is reached;

a flight parameter obtaining module 502, configured to obtain flight parameters of the unmanned aerial vehicle;

and an effective judgment module 503, configured to judge whether the measurement data is effective based on the flight parameter.

In a preferred embodiment of the present invention, the validity determination module 503 may include the following sub-modules:

In a preferred embodiment of the present invention, the basic condition judgment sub-module may include the following units:

In a preferred embodiment of the present invention, the flight parameter includes an altitude at which the drone is located; the jump amount judgment submodule may include the following units:

In a preferred embodiment of the present invention, the designated sensor has an effective counter, and the jump amount determination submodule may include the following units:

In a preferred embodiment of the embodiments of the present invention, the designated sensors include at least a first sensor and a second sensor, and the measurement data includes first measurement data measured by the first sensor and second measurement data measured by the second sensor; the jump variables comprise a first jump quantity corresponding to first measurement data and a second jump quantity corresponding to second measurement data;

the jump amount judgment submodule may include the following units:

In a preferred embodiment of the present invention, the validity judging unit may include the following sub-units:

In a preferred embodiment of the present invention, the apparatus may further include the following modules:

For the apparatus embodiment of fig. 5, since it is basically similar to the method embodiment described above, the description is simple, and for the relevant points, reference may be made to part of the description of the method embodiment.

In addition, the embodiment of the invention also discloses an aircraft, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the steps of the method of the embodiment when executing the program.

In addition, the embodiment of the invention also discloses a computer readable storage medium, on which a computer program is stored, and the program is executed by a processor to realize the steps of the method of the embodiment.

The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.

Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.

The method and the device for detecting data validity provided by the invention are introduced in detail, a specific example is applied in the text to explain the principle and the implementation mode of the invention, and the description of the above embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims

1. A method for detecting data validity is applied to an unmanned aerial vehicle, and is characterized in that the method comprises the following steps:

acquiring flight parameters of the unmanned aerial vehicle;

judging whether the measurement data is valid or not based on the flight parameters;

wherein the step of determining whether the measurement data is valid based on the flight parameter comprises:

judging whether the measurement data meet preset basic conditions or not;

if not, judging that the measurement data is invalid;

if yes, acquiring a jump variable of the measurement data based on the flight parameter, and judging whether the measurement data is valid or not based on the jump variable;

wherein the designated sensor has a valid counter, and the step of determining whether the measurement data is valid based on the jump variable comprises:

2. The method of claim 1, wherein the step of determining whether the measurement data satisfies a predetermined basic condition comprises:

if so, judging that the measurement data does not meet the basic condition;

3. The method of claim 1 or 2, wherein the flight parameters include an altitude at which the drone is located; the step of obtaining the jump variable of the measurement data based on the flight parameter comprises:

4. The method of claim 1, wherein the designated sensors include at least a first sensor and a second sensor, and the measurement data includes first measurement data measured by the first sensor and second measurement data measured by the second sensor; the jump variables comprise a first jump quantity corresponding to first measurement data and a second jump quantity corresponding to second measurement data;

5. The method of claim 4, wherein the step of determining whether the first measurement data is valid based on the first measurement data and the second measurement data comprises:

6. The method of claim 4 or 5, further comprising:

7. The method according to any one of claims 4 or 5, further comprising:

8. The utility model provides a device that data validity detected, is applied to among the unmanned aerial vehicle, its characterized in that, the device includes:

the effective judgment module is used for judging whether the measurement data is effective or not based on the flight parameters;

wherein, the effective judgment module comprises:

the jump quantity judgment submodule is used for acquiring jump quantity of the measurement data based on the flight parameters and judging whether the measurement data is valid or not based on the jump quantity;

wherein the designated sensor has a valid counter, and the jump amount judgment submodule includes:

9. The apparatus of claim 8, wherein the base condition determining sub-module comprises:

a continuous data judgment unit, configured to acquire M pieces of previous measurement data measured at N times before the specified sensor, and if the measurement data is the same as the M pieces of previous measurement data, determine that the measurement data does not satisfy the basic condition; and if the measured data and the M previous measured data are different, judging that the measured data meet the basic condition, wherein N is greater than 1, and M is greater than 1.

10. The apparatus of claim 8 or 10, wherein the flight parameters include an altitude at which the drone is located; the jump amount judgment submodule includes:

11. The apparatus of claim 8, wherein the designated sensors include at least a first sensor and a second sensor, and the measurement data includes first measurement data measured by the first sensor and second measurement data measured by the second sensor; the jump variables comprise a first jump quantity corresponding to first measurement data and a second jump quantity corresponding to second measurement data;

the jump amount judgment submodule includes:

12. The apparatus according to claim 11, wherein the validity judging unit includes:

13. The apparatus of claim 11 or 12, further comprising:

14. The apparatus according to any one of claims 11 or 12, further comprising:

15. An aircraft comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any one of claims 1 to 7 are implemented when the processor executes the program.

16. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.