CN116774570A - Redundancy data analysis method and system - Google Patents
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Abstract
The application provides a redundancy data analysis method and an analysis system, which are applied to an unmanned aerial vehicle, wherein the unmanned aerial vehicle comprises a plurality of sets of flight control systems, each set of flight control system comprises a sensing module, a flight calculation module and a bus module, the flight calculation module acquires data acquired by the corresponding sensing module and transmits the data by the corresponding bus module, and the analysis method comprises the steps of acquiring the data acquired by the sensing modules of all the flight control systems, the control quantity calculated by the flight calculation module and the data quantity received by the bus module; comparing the data collected by all the sensing modules, and screening out the optimal sensing module; comparing the control amounts calculated by all the flight calculation modules, and screening out an optimal flight calculation module; comparing the data quantity received by all bus modules, and screening out the optimal bus module; the optimal sensing module, the optimal flight calculation module and the optimal bus module are combined to form an optimal flight control system, and the optimal flight control system is utilized to operate the unmanned aerial vehicle, so that the stability of the unmanned aerial vehicle is improved.
Description
Technical Field
The application relates to the technical field of unmanned aerial vehicles, in particular to a redundancy data analysis method and system.
Background
Unmanned aerial vehicle refers to unmanned aircraft or aircraft, possesses controllable, execution multitasking and repeatedly usable's characteristic, and various types of unmanned aerial vehicle plays different roles in different fields, and wherein, unmanned aerial vehicle's flight control system is crucial to unmanned aerial vehicle, and once the flight control system is inefficacy in unmanned aerial vehicle flight process, unmanned aerial vehicle's flight mission will not be accomplished and even lead to the crash.
In order to improve the stability of the flight control system, a redundancy technology is generally adopted to design the flight control computer system, wherein the redundancy technology is to set a redundancy part or a backup part exceeding basic requirements in the system, when one part fails, the redundancy technology can be switched to the other part to ensure that the flight control system works normally so as to improve the reliability, fault tolerance and safety of the system, but a plurality of sets of control systems, corresponding sensors and buses exist in the redundancy flight control system, when one part fails, the other set of control system is restarted to work, so that more time is consumed, and the stable flight of the unmanned aerial vehicle is unfavorable.
Therefore, it is necessary to provide a redundancy data analysis method and an analysis system for solving the above technical problems.
Disclosure of Invention
In order to solve the technical problems, the application provides a redundancy data analysis method and an analysis system, which are characterized in that an optimal sensing module is screened out from sensing modules, an optimal flight calculation module is screened out from flight calculation modules, an optimal bus module is screened out from bus modules, the optimal sensing modules, the optimal flight calculation modules and the optimal bus modules form an optimal flight control system, the optimal flight control system is utilized to control the unmanned aerial vehicle to operate, a set of flight control system is prevented from being started to control the unmanned aerial vehicle during faults, the fault removal time is shortened, and the stability of the unmanned aerial vehicle is improved.
The application provides a redundancy data analysis method, which is applied to an unmanned aerial vehicle, wherein the unmanned aerial vehicle comprises a plurality of sets of flight control systems, each set of flight control system comprises a sensing module, a flight calculation module and a bus module, wherein the flight calculation module acquires data acquired by the corresponding sensing module and transmits the data by utilizing the corresponding bus module, and the analysis method comprises the following operation steps:
acquiring data acquired by the sensing modules of the flight control system, control quantity calculated by the flight calculation module and data quantity received by the bus module;
comparing the data collected by all the sensing modules, and screening out the optimal sensing module;
comparing the control amounts calculated by all the flight calculation modules, and screening out an optimal flight calculation module;
comparing the data quantity received by all bus modules, and screening out the optimal bus module;
and combining the optimal sensing module, the optimal flight calculation module and the optimal bus module to form an optimal flight control system, and operating the unmanned aerial vehicle by using the optimal flight control system.
Preferably, the comparing the data collected by all the sensing modules, and screening out the optimal sensing module includes:
collecting data of all sensing modules of the same type;
and determining optimal data, and determining an optimal sensing module according to the optimal data.
Preferably, the determining the optimal data includes:
if the data is of a single type, calculating the average value of the data of the same type;
determining the difference between the data of the same type and the average value;
all the differences are compared to determine the smallest difference, and the data with the smallest difference is determined as the best data.
Preferably, the determining the optimal data includes:
if the data is of a non-single type, calculating the average value of the data of the same type;
the average was considered the best data.
Preferably, before comparing the control amounts calculated by all the flight calculation modules and screening out the optimal flight calculation module, the method comprises the following steps:
the flight calculation module collects data of the optimal sensing module;
the flight calculation module performs flight control calculation on the data of the optimal sensing module to obtain a calculation result;
and acquiring corresponding control quantity according to the calculation result.
Preferably, the comparing the control amounts calculated by all the flight calculation modules, and screening out an optimal flight calculation module includes:
comparing the control quantity calculated by the flight calculation module with the control quantity calculated by the rest flight calculation module according to the set sequence to obtain a comparison result;
if all the comparison results are consistent, selecting one of the flight calculation modules as an optimal flight calculation module;
if one comparison result is inconsistent with other comparison results, judging that the flight calculation module is in a fault state, and continuously comparing the rest flight calculation modules until all comparison results are consistent, and judging that the flight calculation module is the optimal flight calculation module.
Preferably, the comparing the data amounts received by all the bus modules, and screening out the best bus module includes:
counting the data quantity transmitted by each bus module to receive the corresponding flight calculation module;
if all the data amounts are consistent, selecting one of the bus modules as an optimal bus module;
if the data volume received by one bus module is inconsistent with the data volume received by other bus modules, judging that the bus module is in a fault state, and selecting any remaining bus module as the optimal bus module.
Preferably, the combining the optimal sensing module, the optimal flight calculation module and the optimal bus module includes:
establishing signal channels among the optimal sensing module, the optimal flight calculation module and the optimal bus module;
and transmitting data among the optimal sensing module, the optimal flight calculation module and the optimal bus module in sequence by utilizing the signal channel.
The application also provides a redundancy data analysis system, comprising:
the acquisition unit is used for acquiring data acquired by the sensing modules of all the flight control systems, the control quantity calculated by the flight calculation module and the data quantity received by the bus module;
the data comparison unit is used for comparing the data acquired by all the sensing modules and screening out the optimal sensing module;
the control quantity comparison unit is used for comparing the control quantities calculated by all the flight calculation modules and screening out the optimal flight calculation module;
the data volume comparison unit is used for comparing the data volumes received by all the bus modules and screening out the optimal bus module;
and the combination unit is used for combining the optimal sensing module, the optimal flight calculation module and the optimal bus module to form an optimal flight control system, and the optimal flight control system is used for controlling the unmanned aerial vehicle to operate.
Preferably, the system further comprises a signal channel establishment unit for establishing a signal channel among the optimal sensing module, the optimal flight calculation module and the optimal bus module.
Compared with the related art, the redundancy data analysis method and the redundancy data analysis system provided by the application have the following beneficial effects:
according to the application, the best sensing module is screened out from the sensing modules, the best flight calculation module is screened out from the flight calculation modules, the best bus module is screened out from the bus modules, the best sensing module, the best flight calculation module and the best bus module form the best flight control system, the best flight control system is utilized to operate the unmanned aerial vehicle, a set of flight control system is prevented from being started to operate the unmanned aerial vehicle during faults, the fault removal time is shortened, and the stability of the unmanned aerial vehicle is improved.
Drawings
FIG. 1 is a flow chart of a redundancy data analysis method according to the present application;
FIG. 2 is a schematic diagram of a screening flow of an optimal sensing module according to the redundancy data analysis method of the present application;
FIG. 3 is a schematic diagram of a screening flow of an optimal flight calculation module according to the redundancy data analysis method of the present application;
FIG. 4 is a schematic diagram of a screening flow of an optimal bus module according to the redundancy data analysis method of the present application;
FIG. 5 is a schematic diagram of an optimal flight control system acquisition flow for a redundancy data analysis method according to the present application;
fig. 6 is a schematic structural diagram of a redundancy data analysis system according to the present application.
Detailed Description
The application will be further described with reference to the drawings and embodiments.
Example 1
The application provides a redundancy data analysis method, which is applied to the field of unmanned aerial vehicles, wherein the unmanned aerial vehicle adopts redundancy technology and comprises a plurality of sets of flight control systems, each set of flight control system consists of a sensing module, a flight calculation module and a bus module, and when the unmanned aerial vehicle is used, the sensing module acquires data, the flight calculation module acquires the data acquired by the sensing module and transmits the data acquired by the sensing module by using the bus module.
Referring to fig. 1, the analysis method of the present application comprises the following steps:
step 100: and acquiring data acquired by the sensing modules of all the flight control systems, the control quantity calculated by the flight calculation module and the data quantity received by the bus module.
In this embodiment, each flight control system includes a corresponding sensing module, a flight calculation module and a bus module, when the unmanned aerial vehicle works, the sensing module of each set of flight control system can acquire the same type of data, then the corresponding flight calculation module acquires the data, and finally the data is transmitted through the bus module.
Step 200: and comparing the data acquired by all the sensing modules, and screening out the optimal sensing module.
In this embodiment, referring to fig. 2, step 200 specifically includes:
step 201: data is collected for all sensing modules of the same type.
In this embodiment, in order to determine the optimal sensing module, it is necessary to collect data of the same type of sensing module and determine the optimal sensing module between the sensing modules.
In practical application, the data is a variable acquired by a sensor, and under different application scenes, the variable can be single, namely one variable can represent the required information; it is also possible to be non-unitary, i.e. there are a plurality of variables, which together represent the required information.
Step 202: and determining optimal data, and determining an optimal sensing module according to the optimal data.
When the method is applied specifically, the determination process of the optimal data is periodic, and the periodic determination can continuously provide accurate data for the flight of the unmanned aerial vehicle so as to ensure that the unmanned aerial vehicle can stably run.
In this embodiment, the data collected by the sensing module may be classified into a single type and a non-single type, and the processing of the two data types is also different.
If the data is of the single type, first, the average of all the data of the same type is calculated.
Second, after the average value is calculated, all data are differenced from the average value, and the difference between all data and the average value is determined.
And finally, finding out the data corresponding to the difference value with the smallest value from all the difference values, and identifying the data as the optimal data.
If the data is of a non-unitary type, it is only necessary to calculate the average value of the same type of data and consider the average value as the best data.
After determining the optimal sensing module according to the optimal data, the data acquired by the optimal sensing module needs to be packaged and sent to the flight calculation modules, specifically, each flight calculation module performs flight control calculation by utilizing the acquired data of the optimal sensing module, obtains a calculation result, and finally obtains a corresponding control quantity according to the calculation result.
Step 300: comparing the control amounts calculated by all the flight calculation modules, and screening out an optimal flight calculation module;
in this embodiment, referring to fig. 3, step 300 specifically includes:
step 301: and comparing the control quantity calculated by the flight calculation module with the control quantity calculated by the rest flight calculation module according to the set sequence to obtain a comparison result.
Specifically, the setting sequence is 1, 2, 3, … …, that is, the flight calculation modules are numbered in sequence, and the flight calculation modules are compared with the remaining flight calculation modules in the sequence, for example: and (3) comparing the control quantity of the flight calculation module with the sequence number of 1 with the control quantity of the rest flight calculation modules with the sequence numbers of 2, 3 and … …, and then comparing the control quantity of the flight calculation module with the sequence number of 2 with the control quantity of the rest flight calculation modules with the sequence numbers of 3, 4 and … … after the comparison of the flight calculation modules with the sequence number of 1, and so on.
The comparison process between the flight calculation modules specifically comprises the following steps: each flight calculation module encapsulates the acquired control quantity into a data packet, the data packet is respectively transmitted to other flight calculation modules for data comparison through the corresponding bus modules, and meanwhile, each flight calculation module analyzes the control quantity data packet transmitted by the other flight calculation modules and received through the corresponding bus modules to acquire the corresponding control quantity.
Step 302: and if all the comparison results are consistent, selecting one of the flight calculation modules as an optimal flight calculation module.
Specifically, a threshold value may be set for the comparison result, if the comparison result meets the threshold value, it indicates that all comparison results are approved, and in this case, the best flight calculation module needs to be selected according to the set sequence number, that is, the flight calculation module with the sequence number of 1.
Step 303: if one comparison result is inconsistent with other comparison results, judging that the flight calculation module is in a fault state, and continuously comparing the rest flight calculation modules until all comparison results are consistent, and judging that the flight calculation module is the optimal flight calculation module.
Specifically, in the comparison process, if one comparison result is inconsistent with other comparison results, the comparison flight calculation module is in a fault state and needs to be subjected to silence treatment; in order to find out the optimal flight calculation module, comparison is needed to be continued until the comparison results are consistent, the compared flight calculation module is considered to be the optimal flight calculation module, and after the comparison results are consistent, the rest flight calculation modules do not need to be compared, so that the comparison time is saved.
In this embodiment, referring to fig. 4, step 400: comparing the data quantity received by all bus modules, screening out the best bus module, specifically comprising:
step 401: and counting the data quantity transmitted by each bus module to receive the corresponding flight calculation module.
Specifically, in the process of transferring the control amount through the bus modules, each bus module receives the control amount transferred by the corresponding flight calculation module, and needs to count the control amount and convert the control amount into a data amount.
Step 402: if all the data amounts are consistent, one of the bus modules is selected as the optimal bus module.
When the data quantity received by all the bus modules is consistent, indicating that all the bus modules are in a normal state, optionally selecting one of the bus modules as an optimal bus module, wherein the specific selection method can refer to the numbering method of the optimal flight calculation module, numbering the bus modules, and selecting according to the sequence number priority.
Step 403: if the data volume received by one bus module is inconsistent with the data volume received by other bus modules, judging that the bus module is in a fault state, and selecting any remaining bus module as the optimal bus module.
Specifically, if the data volume transmitted by the rest flight calculation modules which cannot be received by a certain flight calculation module through the corresponding bus module is inconsistent with the data volume received by other bus modules, determining that the corresponding bus module is in a fault state, and silencing the bus module.
In this embodiment, referring to fig. 1, step 500: and combining the optimal sensing module, the optimal flight calculation module and the optimal bus module to form an optimal flight control system, and operating the unmanned aerial vehicle by using the optimal flight control system.
Specifically, referring to fig. 5, after the optimal sensing module, the optimal flight calculation module, and the optimal bus module are determined, signal paths between the optimal sensing module, the optimal flight calculation module, and the optimal bus module are established.
And transmitting data among the optimal sensing module, the optimal flight calculation module and the optimal bus module in sequence by utilizing the signal channel.
The working principle of the redundancy data analysis method provided by the application is as follows: according to the application, the best sensing module is screened out from the sensing modules, the best flight calculation module is screened out from the flight calculation modules, the best bus module is screened out from the bus modules, the best sensing module, the best flight calculation module and the best bus module form the best flight control system, the best flight control system is utilized to operate the unmanned aerial vehicle, a set of flight control system is prevented from being started to operate the unmanned aerial vehicle during faults, the fault removal time is shortened, and the stability of the unmanned aerial vehicle is improved.
Example two
The application also provides a redundancy data analysis system, referring to fig. 6, which specifically comprises an acquisition unit, a data comparison unit, a control quantity comparison unit, a data quantity comparison unit and a data quantity comparison unit, wherein,
the acquisition unit is used for acquiring data acquired by the sensing modules of all the flight control systems, the control quantity calculated by the flight calculation module and the data quantity received by the bus module.
Specifically, each flight control system comprises a corresponding sensing module, a flight calculation module and a bus module, when the unmanned aerial vehicle works, the sensing module of each set of flight control system can acquire the same type of data, then the corresponding flight calculation module acquires the data, finally the data is transmitted through the bus module, and in the process, the acquisition unit sequentially acquires the data acquired by the sensing module, the control quantity calculated by the flight calculation module and the data quantity received by the bus module.
The data comparison unit is used for comparing the data acquired by all the sensing modules and screening out the optimal sensing module.
Specifically, the data comparison unit collects data of all the sensing modules of the same type.
In this embodiment, in order to determine the optimal sensing module, the data comparing unit needs to collect data of the sensing modules of the same type and determine the optimal sensing module between the sensing modules.
In practical application, the data is a variable acquired by a sensor, and under different application scenes, the variable can be single, namely one variable can represent the required information; it is also possible to be non-unitary, i.e. there are a plurality of variables, which together represent the required information.
The data comparison unit determines optimal data and determines an optimal sensing module according to the optimal data.
When the method is applied specifically, the determination process of the optimal data is periodic, and the periodic determination can continuously provide accurate data for the flight of the unmanned aerial vehicle so as to ensure that the unmanned aerial vehicle can stably run.
In this embodiment, the data collected by the sensing module may be classified into a single type and a non-single type, and the processing of the two data types is also different.
If the data is of the single type, first, the data comparison unit calculates the average value of all the data of the same type.
And secondly, after calculating the average value, the data comparison unit makes a difference between all data and the average value, and determines the difference between all data and the average value.
And finally, the data comparison unit finds out the data corresponding to the difference value with the smallest value from all the difference values, and considers the data as the optimal data.
If the data is of a non-unitary type, the data comparison unit only needs to calculate the average value of the data of the same type and identify the average value as the optimal data.
After determining the optimal sensing module according to the optimal data, the data acquired by the optimal sensing module needs to be packaged and sent to the flight calculation modules, specifically, each flight calculation module performs flight control calculation by utilizing the acquired data of the optimal sensing module, obtains a calculation result, and finally obtains a corresponding control quantity according to the calculation result.
The control quantity comparison unit is used for comparing the control quantity calculated by all the flight calculation modules and screening out the optimal flight calculation module;
specifically, first, the control amount comparison unit compares the control amount calculated by the flight calculation module with the control amount calculated by the rest of the flight calculation modules according to the set sequence, and a comparison result is obtained.
Specifically, the setting sequence is 1, 2, 3, … …, that is, the flight calculation modules are numbered in sequence, and the control amount comparison unit compares the flight calculation modules with the remaining flight calculation modules in this sequence, for example: the control quantity comparison unit compares the control quantity of the flight calculation module with the sequence number of 1 with the control quantity of the rest flight calculation modules with the sequence numbers of 2, 3 and … …, and after the flight calculation module with the sequence number of 1 is compared, the control quantity comparison unit then compares the control quantity of the flight calculation module with the sequence number of 2 with the control quantity of the rest flight calculation modules with the sequence numbers of 3, 4 and … …, and so on.
The comparison process between the flight calculation modules specifically comprises the following steps: each flight calculation module encapsulates the acquired control quantity into a data packet, the data packet is respectively transmitted to other flight calculation modules for data comparison through the corresponding bus modules, and meanwhile, each flight calculation module analyzes the control quantity data packet transmitted by the other flight calculation modules and received through the corresponding bus modules to acquire the corresponding control quantity.
And secondly, if the control quantity comparison unit judges that all comparison results are consistent, selecting one of the flight calculation modules as an optimal flight calculation module.
Specifically, a threshold value may be set for the comparison result, if the comparison result meets the threshold value, it indicates that all comparison results are approved, and in this case, the best flight calculation module needs to be selected according to the set sequence number, that is, the flight calculation module with the sequence number of 1.
And finally, if the control quantity comparison unit judges that one comparison result is inconsistent with other comparison results, judging that the flight calculation module is in a fault state, and continuously comparing the rest flight calculation modules until all comparison results are consistent, and judging that the flight calculation module is the optimal flight calculation module.
Specifically, in the comparison process, if the control quantity comparison unit judges that one comparison result is inconsistent with other comparison results, the control quantity comparison unit indicates that the compared flight calculation module is in a fault state and needs to carry out silence treatment; in order to find out the optimal flight calculation module, comparison is needed to be continued until the comparison results are consistent, the compared flight calculation module is considered to be the optimal flight calculation module, and after the comparison results are consistent, the rest flight calculation modules do not need to be compared, so that the comparison time is saved.
The data volume comparison unit is used for comparing the data volumes received by all the bus modules and screening out the optimal bus module.
Specifically, the data volume comparison unit counts the data volume transmitted by each bus module to receive the corresponding flight calculation module.
Specifically, first, in the process of transferring the control amount through the bus modules, each bus module receives the control amount transferred by the corresponding flight calculation module, and the data amount comparison unit needs to count the control amount and convert the control amount into the data amount.
And secondly, if the data quantity comparison unit judges that all the data quantities are consistent, one of the bus modules is selected as the optimal bus module.
When the data quantity comparison unit judges that the data quantity received by all the bus modules is consistent, the data quantity comparison unit indicates that all the bus modules are in a normal state, one bus module is selected as an optimal bus module, a specific selection method can refer to a numbering method of an optimal flight calculation module, the bus modules are numbered, and the bus modules are selected according to the priority of serial numbers.
And finally, if the data quantity comparison unit judges that the data quantity received by one bus module is inconsistent with the data quantity received by other bus modules, judging that the bus module is in a fault state, and selecting any remaining bus module as an optimal bus module.
Specifically, if the data volume comparison unit determines that the data volume transmitted by the rest of flight calculation modules which cannot be received by a certain flight calculation module through the corresponding bus module is not consistent with the data volume received by other bus modules, the corresponding bus module is determined to be in a fault state, and the bus module is silenced.
The combination unit is used for combining the optimal sensing module, the optimal flight calculation module and the optimal bus module to form an optimal flight control system, and the optimal flight control system is used for controlling the unmanned aerial vehicle to operate.
Specifically, after determining the optimal sensing module, the optimal flight calculation module, and the optimal bus module, the combining unit establishes signal paths between the optimal sensing module, the optimal flight calculation module, and the optimal bus module.
The combination unit sequentially transmits data among the optimal sensing module, the optimal flight calculation module and the optimal bus module by utilizing the signal channel.
In addition, the signal channel is established by using a signal channel establishing unit, and the signal channel can be transmitted in a wired mode or in a wireless mode.
The working principle of the redundancy data analysis system provided by the application is as follows: the data comparison unit, the control quantity comparison unit and the data quantity comparison unit respectively screen out the optimal sensing module from the sensing modules, screen out the optimal flight calculation module from the flight calculation module and screen out the optimal bus module from the bus module, finally, the optimal sensing module, the optimal flight calculation module and the optimal bus module form an optimal flight control system by utilizing the combination unit, the optimal flight control system is utilized to control the unmanned aerial vehicle to operate, a set of flight control system is prevented from being started to control the unmanned aerial vehicle during faults, the fault elimination time is shortened, and the stability of the unmanned aerial vehicle is improved.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Those of ordinary skill in the art will appreciate that all or part of the steps of the various methods of the above embodiments may be implemented by hardware associated with a program stored in a computer-readable storage medium, including Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (CD-ROM), or other optical disk Memory, magnetic disk Memory, tape Memory, or any other medium capable of being used for carrying or storing data that can be Read by a computer.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
Claims (10)
1. The redundancy data analysis method is characterized by being applied to an unmanned aerial vehicle, wherein the unmanned aerial vehicle comprises a plurality of sets of flight control systems, each set of flight control system comprises a sensing module, a flight calculation module and a bus module, the flight calculation module collects data collected by the corresponding sensing module and transmits the data by the corresponding bus module, and the analysis method comprises the following operation steps:
acquiring data acquired by the sensing modules of the flight control system, control quantity calculated by the flight calculation module and data quantity received by the bus module;
comparing the data collected by all the sensing modules, and screening out the optimal sensing module;
comparing the control amounts calculated by all the flight calculation modules, and screening out an optimal flight calculation module;
comparing the data quantity received by all bus modules, and screening out the optimal bus module;
and combining the optimal sensing module, the optimal flight calculation module and the optimal bus module to form an optimal flight control system, and operating the unmanned aerial vehicle by using the optimal flight control system.
2. The method for analyzing redundancy data according to claim 1, wherein comparing the data collected by all the sensing modules to screen out the optimal sensing module comprises:
collecting data of all sensing modules of the same type;
and determining optimal data, and determining an optimal sensing module according to the optimal data.
3. The method of claim 2, wherein said determining optimal data comprises:
if the data is of a single type, calculating the average value of the data of the same type;
determining the difference between the data of the same type and the average value;
all the differences are compared to determine the smallest difference, and the data with the smallest difference is determined as the best data.
4. The method of claim 2, wherein said determining optimal data comprises:
if the data is of a non-single type, calculating the average value of the data of the same type;
the average was considered the best data.
5. The method of claim 1, wherein the step of comparing the control amounts calculated by all the flight calculation modules to select the optimal flight calculation module comprises:
the flight calculation module collects data of the optimal sensing module;
the flight calculation module performs flight control calculation on the data of the optimal sensing module to obtain a calculation result;
and acquiring corresponding control quantity according to the calculation result.
6. The method for analyzing redundancy data according to claim 5, wherein said comparing the control amounts calculated by all the flight calculation modules to screen out the optimal flight calculation module comprises:
comparing the control quantity calculated by the flight calculation module with the control quantity calculated by the rest flight calculation module according to the set sequence to obtain a comparison result;
if all the comparison results are consistent, selecting one of the flight calculation modules as an optimal flight calculation module;
if one comparison result is inconsistent with other comparison results, judging that the flight calculation module is in a fault state, and continuously comparing the rest flight calculation modules until all comparison results are consistent, and judging that the flight calculation module is the optimal flight calculation module.
7. The method for analyzing redundancy data according to claim 1, wherein said comparing the data amounts received by all bus modules and screening out the best bus module comprises:
counting the data quantity transmitted by each bus module to receive the corresponding flight calculation module;
if all the data amounts are consistent, selecting one of the bus modules as an optimal bus module;
if the data volume received by one bus module is inconsistent with the data volume received by other bus modules, judging that the bus module is in a fault state, and selecting any remaining bus module as the optimal bus module.
8. The method of claim 1, wherein said combining said optimal sensing module, optimal flight calculation module and optimal bus module comprises:
establishing signal channels among the optimal sensing module, the optimal flight calculation module and the optimal bus module;
and transmitting data among the optimal sensing module, the optimal flight calculation module and the optimal bus module in sequence by utilizing the signal channel.
9. A redundancy data analysis system, the analysis system comprising:
the acquisition unit is used for acquiring data acquired by the sensing modules of all the flight control systems, the control quantity calculated by the flight calculation module and the data quantity received by the bus module;
the data comparison unit is used for comparing the data acquired by all the sensing modules and screening out the optimal sensing module;
the control quantity comparison unit is used for comparing the control quantities calculated by all the flight calculation modules and screening out the optimal flight calculation module;
the data volume comparison unit is used for comparing the data volumes received by all the bus modules and screening out the optimal bus module;
and the combination unit is used for combining the optimal sensing module, the optimal flight calculation module and the optimal bus module to form an optimal flight control system, and the optimal flight control system is used for controlling the unmanned aerial vehicle to operate.
10. The redundancy data analysis system of claim 9, further comprising a signal path establishing unit for establishing a signal path between the optimal sensing module, the optimal flight calculation module, and the optimal bus module.
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