CN113759790A - System optimization method and device for unmanned equipment - Google Patents
System optimization method and device for unmanned equipment Download PDFInfo
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Abstract
The specification discloses a method and a device for optimizing a system of unmanned equipment, which can acquire the consumed time of each module for processing each message when the unmanned equipment executes a task, perform Gaussian fitting on the stored consumed time corresponding to each module after the unmanned equipment executes the task, determine the consumed time distribution of each module, determine a confidence interval corresponding to each module according to the consumed time distribution and an abnormal consumed time threshold value, determine a standard execution time corresponding to each module according to each consumed time of each module, and send prompt information for prompting that the module has a fault and needs to be optimized when the module has the fault according to the confidence interval and the standard execution time of each module. According to the method, whether the time consumed by each module for processing the message is too long or not is judged through the time consumed by each module for processing the message, if yes, the module is considered to have a fault, and when the fault exists but is not shown, the module with the fault can be determined, so that the driving safety of the unmanned equipment is guaranteed.
Description
Technical Field
The specification relates to the technical field of computers, in particular to a system optimization method and device for unmanned equipment.
Background
Currently, along with the development of computer technology, unmanned equipment is increasingly used in daily life of people. In order to ensure the driving safety of the unmanned device, the unmanned device is generally required to be optimized.
Generally, the unmanned aerial vehicle can be optimized according to faults occurring during driving. Specifically, the service provider can monitor a running track, an acquired image and the like of the unmanned device in the running process, judge whether a fault occurs according to the monitored data, and when the fault occurs, a worker of the service provider can take over the unmanned device. Subsequently, the fault of the unmanned equipment can be checked, the corresponding fault point is determined, and then the system in the unmanned equipment is optimized.
However, in the prior art, when optimization is performed, the unmanned device is optimized only when a fault occurs, and when the unmanned device has a fault but does not show a corresponding representation (for example, a trajectory is drawn, and the like), it cannot be determined that the unmanned device has a fault and cannot be optimized according to the fault, so that the unmanned device has a large potential safety hazard.
Disclosure of Invention
The present specification provides a method and apparatus for system optimization of an unmanned aerial vehicle to partially solve the above problems in the prior art.
The technical scheme adopted by the specification is as follows:
the present specification provides a method for optimizing a system of an unmanned aerial vehicle, the system comprising a plurality of modules, including:
when the unmanned equipment executes a task, monitoring the consumed time of each module in the system when the module processes the message each time, and storing the consumed time;
after the unmanned equipment completes the task, performing Gaussian fitting on the stored time consumed by each module when processing each message, and determining the processing time consumption distribution corresponding to each module;
determining a confidence interval of each processing time consumption distribution corresponding to the abnormal time consumption threshold according to a preset abnormal time consumption threshold, and determining a standard execution time corresponding to each module according to the time consumption of each module for processing each message;
and when the module is determined to have a fault according to the determined confidence interval and the standard execution time of each module, sending prompt information for prompting that the module has the fault and needs to be optimized.
Optionally, according to a preset abnormal time-consuming threshold, determining a confidence interval of each processing time-consuming distribution corresponding to the abnormal time-consuming threshold, specifically including:
for each module, determining a mean value and a variance corresponding to the processing time consumption distribution corresponding to the module according to the processing time consumption distribution corresponding to the module;
and determining a confidence interval of the processing time consumption distribution according to the mean value, the variance and a preset abnormal time consumption threshold.
Optionally, when it is determined that a module has a fault according to the determined confidence interval and the determined standard execution time of each module, sending a prompt message for prompting that the module has the fault and needs to be optimized, specifically including:
for each module, determining the corresponding abnormal time consumption of the module according to the confidence interval;
when the standard execution time is not less than the abnormal time consumption, determining that the module has a fault;
and sending prompt information for prompting that the module has a fault and needs to be optimized according to the module identifier of the module.
Optionally, the method further comprises:
receiving an optimization request while the unmanned device is performing a task;
determining the time consumed by the module for processing each message according to the module identifier carried in the optimization request;
and determining the processing time consumption distribution of the module according to the time consumption of the module for processing each message.
Optionally, the method further comprises:
storing the time consumed by each module for processing each message according to the message identifier of each message;
for each module, determining the abnormal time consumption of the module for processing the message according to the confidence interval, and determining the message with the time consumption not less than the abnormal time consumption as the abnormal message when the module has a fault;
and sending prompt information according to the determined abnormal message.
Optionally, monitoring time consumed by the module when the module performs message processing each time, and storing the time consumed by the module specifically includes:
and acquiring and storing the consumed time of the module in each message processing by aiming at the monitoring code corresponding to the module embedded in the system in advance.
Optionally, there are multiple versions of the system in the unmanned device, the method further comprising:
determining historical abnormal time consumption corresponding to each module according to the abnormal time consumption corresponding to each module of the historical version of the system;
determining the abnormal time consumption corresponding to each module according to the determined confidence interval corresponding to each module;
and when the abnormal time consumption of each module is not less than the historical abnormal time consumption, sending prompt information.
The present specification provides a system optimization apparatus for an unmanned aerial vehicle, including:
the acquisition module is used for monitoring the consumed time of each module in the system when the unmanned equipment executes a task and storing the consumed time;
the first determining module is used for performing Gaussian fitting on the stored time consumed by each module when processing each message after the unmanned equipment executes the task, and determining the processing time consumption distribution corresponding to each module;
the second determining module is used for determining a confidence interval of each processing time consumption distribution corresponding to the abnormal time consumption threshold according to a preset abnormal time consumption threshold, and determining standard execution time corresponding to each module according to the time consumption of each module in processing each message;
and the prompting module is used for sending prompting information for prompting that the module has the fault and needs to be optimized when the module has the fault according to the determined confidence interval and the standard execution time of each module.
The present specification provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described method for system optimization of an unmanned aerial device.
The electronic device provided by the specification comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the system optimization method of the unmanned device.
The technical scheme adopted by the specification can achieve the following beneficial effects:
in the system optimization method for the unmanned aerial vehicle provided in this specification, when the unmanned aerial vehicle executes a task, time consumed by each module for processing each message is obtained, after the unmanned aerial vehicle executes the task, gaussian fitting is performed on each stored time consumed corresponding to each module, processing time consumption distribution of each module is determined, a confidence interval corresponding to each module is determined according to the processing time consumption distribution and an abnormal time consumption threshold, a standard execution time corresponding to each module is determined according to each time consumed by each module, and when a module has a fault according to the confidence interval and the standard execution time of each module, prompt information for prompting that the module has the fault and needs to be optimized is sent.
According to the method, the time consumed by each module for processing each message is used for judging whether the time consumed by each module for processing the message is too long, if so, the module is considered to have a fault, and when the fault exists but is not shown, the method can determine the module with the fault, so that the driving safety of the unmanned equipment is ensured.
Drawings
The accompanying drawings, which are included to provide a further understanding of the specification and are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description serve to explain the specification and not to limit the specification in a non-limiting sense. In the drawings:
FIG. 1 is a schematic flow diagram of a method for system optimization of an unmanned aerial device as provided herein;
FIG. 2 is a schematic diagram of monitoring data of modules in acquiring messages processed by the system of the unmanned aerial vehicle provided by the present description;
FIG. 3 is a schematic illustration of a confidence interval for determining an abnormal elapsed time threshold provided herein;
FIG. 4 is a schematic diagram of a system optimization apparatus for an unmanned aerial vehicle provided herein;
fig. 5 is a schematic diagram of an electronic device corresponding to fig. 1 provided in the present specification.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more clear, the technical solutions of the present disclosure will be clearly and completely described below with reference to the specific embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort belong to the protection scope of the present specification.
Currently, systems are provided in unmanned devices to control the operation of the unmanned devices. In order to ensure the driving safety of the unmanned equipment, the unmanned equipment needs to be monitored. Generally, monitoring of the unmanned device is to determine whether a fault occurs based on an image acquired during driving of the unmanned device, a driving track of the unmanned device, and the like.
Taking the unmanned device to execute the distribution task as an example, whether the unmanned device shakes or not can be judged according to the blurring degree and the like of the image collected by the unmanned device. And judging whether the unmanned equipment has the fault condition of drawing a dragon on the track or not through the running track of the unmanned equipment. And judging whether the fault condition that the motion strategy at the next moment cannot be determined occurs or not according to the information reported by the unmanned equipment.
If any judgment result is yes, the service provider staff can take over the unmanned equipment, and fault troubleshooting is carried out on the unmanned equipment and a fault point is determined according to the fault representation.
The reason for the above described fault appearance is often that the time taken for the modules of the system in the drone to process the message is lengthy. Factors that may cause the module to spend too much time processing a message may include: the core function defects of the modules, excessive I/O times, network faults, resource competition and the like, the modules which are caused by different reasons and have overlong time consumption are not completely the same, and different fault representations can occur when different modules process messages and have overlong time consumption.
In this regard, the present specification provides a method that can optimize a system of unmanned devices based on the time it takes for the modules of the system to process messages.
The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a system optimization method for an unmanned aerial vehicle provided in this specification, specifically including the following steps:
s100: and when the unmanned equipment executes a task, monitoring the time consumption of each module in the system when the module processes the message each time, and storing the time consumption.
In one or more embodiments provided in this specification, the method for optimizing the system of the unmanned aerial vehicle aims at a scene that when the unmanned aerial vehicle performs a task, each module in the system of the unmanned aerial vehicle controls the unmanned aerial vehicle so that the unmanned aerial vehicle completes the task. The system optimization method flow of the unmanned device can be specifically executed by the unmanned device or a server of a service provider. For convenience of description, the following description will be given taking an unmanned aerial vehicle as an example.
Different from the prior art, the method optimizes the unmanned equipment according to the faults occurring in the driving process of the unmanned equipment. The present specification provides a method for optimizing a system based on the time consumed by each module of the system for unmanned equipment to process a message, so that the unmanned equipment can determine whether the time consumed by each module in the system for processing a message is too long based on the time consumed by each module in the system for unmanned equipment to perform troubleshooting, and optimize according to the determined fault.
Based on this, in one or more embodiments herein, the elapsed time for each message processing by each module in the system of the drone may be monitored and stored for each module as the drone performs a task.
Specifically, each module of the system of the unmanned aerial vehicle has a corresponding code, and when the system receives a message, the system can determine a module corresponding to the message according to a module identifier carried in the received message. And sending the message to the module to enable the module to process the message. And embedding monitoring codes in front of and behind each module, namely determining the time consumption of each message processing of the module by acquiring the running time of the monitoring codes.
Thus, the drone may determine, by monitoring the code, the elapsed time for each module to process a message each time each module processes a message. As shown in fig. 2.
Fig. 2 is a schematic diagram for determining the time consumption of each module in each message processing, provided in the present specification, in which a module a, a module B, a module C, and a module D … … are modules in a system of the unmanned aerial vehicle, and monitoring codes are pre-embedded in the system for each module. After receiving the message, the unmanned device may determine, according to the module identifier carried in the message, a module corresponding to the message, where the module identifier carried in the message 1 in the drawing is B, so that the system of the unmanned device may send the message 1 to the module B, and the module B processes the message 1 to obtain a processing result. Then, based on the monitor code before module B and the monitor code after module B, it may be determined that the module takes 3 minutes and 3 seconds to process the message, and the drone may then determine that the module takes 00:03:03 to process the message.
Of course, since the consumed time can be determined by the processing start time and the processing end time, and the unmanned device usually needs computing resources to determine a motion strategy and the like during driving, in order to ensure the driving safety of the unmanned device, the processing start time and the processing end time corresponding to the processing of the message by the modules can be stored, and the consumed time of each module during processing of each message can be determined after the task is executed.
In one or more embodiments provided in this specification, as described above, the reason why the time consumption is too long when different modules perform message processing and the failure that may be caused are not completely the same, but the time consumption for processing messages and the like of different modules are not completely the same, and therefore, the unmanned aerial vehicle may store, for each module, the time consumption for each message processing of the module, so that the module that has failed may be determined based on the stored time consumption for each message processing of the module in the following.
Specifically, the unmanned aerial vehicle device may store the determined consumed time of the module during each message processing for each module according to the module identifier of the module. The stored time-consuming form for each module to perform message processing may include an identifier of each module, a start execution time, an end time, and the like of the message, and may be specifically as shown in table 1.
| [ Module identification ]]: [ Start time]: [ end time] |
| [ Module identification ]]：[cost]: [ duration of time] |
TABLE 1
The module identifier is an identifier of the module, such as the module a in fig. 2, where a is the identifier of the module, and the processing start time and the processing end time of a certain module can be determined according to the [ start time ] and the [ end time ] portions, so as to determine the time consumed by the module in message processing. The duration is the time consumed by the module for message processing, and the unmanned equipment can also determine a storage form only according to the module identification and the time consumed and store the storage form.
Because the two methods for acquiring the execution time of the message by the data are not consistent, in order to distinguish the two monitoring data, a [ cost ] item is added into the monitoring data determined according to the duration for representing the duration consumed by the monitoring data for processing the message.
In addition, since the determined time consumed for each module to perform message processing is determined according to the monitoring codes before and after the module, a situation that the processing start time of the previous message is taken as the processing start time of the message may occur, and therefore, in order to ensure the accuracy of the monitoring data and avoid the situation, when the unmanned device stores the monitoring data, the time consumed for each module to perform message processing can be stored according to the message identifier and the module identifier. As shown in table 2.
| [ Module identification ]]: [ message identification ]]: [ Start time]: [ date of day] |
| [ Module identification ]]: [ message identification ]]: [ end time]: [ date of day] |
| [ Module identification ]]: [ message identification ]]：[cost]: [ duration of time] |
TABLE 2
The message identifier is an identifier of a processed message, for example, "message 1" in fig. 2, and may be carried by the message itself, or may be determined by a module for processing the message or an unmanned device, for example, the message identifier of the 32 th message processed by the module a is 32, and the [ start time ] and [ end time ] part are used together with the following [ date ] part, so that the specific start processing time and the specific end processing time of a certain module may be determined, and further the time consumption when a certain module processes the message may be determined.
S102: and after the unmanned equipment executes the task, performing Gaussian fitting on the stored time consumption of each module in processing each message, and determining the processing time consumption distribution corresponding to each module.
In one or more embodiments provided in this specification, when the unmanned aerial vehicle executes different delivery tasks, the corresponding execution times of the unmanned aerial vehicle may be different, and therefore, in order to ensure the accuracy of the determined fault result, the unmanned aerial vehicle may optimize the system according to the stored time consumed when each module processes each message after the unmanned aerial vehicle executes the task.
Specifically, first, the unmanned aerial vehicle may obtain, for each task, time consumed when each module stored in the system of the unmanned aerial vehicle processes each message during the process of executing the task after the task is executed.
Then, after determining the time consumed by each module for processing each message, the unmanned device may perform gaussian fitting on the time consumed by each module for processing each message, and obtain the processing time consumption distribution corresponding to the module through fitting. Obviously, the processing time-consuming distribution is a gaussian distribution.
Finally, in the subsequent step S106, it may be determined whether there is a failure based on the processing time consumption distribution of each module.
S104: and determining a confidence interval of the consumed time distribution of each message corresponding to the abnormal consumed time threshold according to a preset abnormal consumed time threshold, and determining the standard execution time corresponding to each module according to the consumed time of each module for processing each message.
In one or more embodiments provided in this specification, for each module, the module performs a processing time consumption distribution determined by time consumption fitting when the module processes each message, and is a time distribution when the module processes each message and is also a probability distribution of a processing time of the module for each message, and if the module processes most of messages, that is, the module's standard execution time, corresponds to a smaller probability interval in the probability distribution, the execution time of the module is obviously too long. Therefore, the unmanned device can determine confidence intervals of the processing time consumption distributions corresponding to the abnormal time consumption threshold and standard execution time of each module based on the preset abnormal time consumption threshold.
Specifically, for each module, the unmanned aerial vehicle may determine, according to the processing time consumption distribution corresponding to the module determined in step S102 and a preset abnormal time consumption threshold, a confidence interval corresponding to the processing time consumption distribution when the probability distribution is the abnormal time consumption threshold. As shown in fig. 3.
Fig. 3 is a schematic diagram of determining a confidence interval of an abnormal elapsed time threshold provided in this specification, where a black dot is elapsed time of each message processing performed by a module, a curve is a processing elapsed time distribution obtained by performing gaussian fitting on each elapsed time, and the abnormal elapsed time threshold is 85%, so that the unmanned aerial device can determine, based on the determined processing elapsed time distribution, that the confidence interval corresponding to the abnormal elapsed time threshold is [12, 38] ms.
Then, the unmanned device can determine the standard execution time corresponding to each module according to the stored time consumed by each module for processing each message, namely, the minimum time consumed by each module for processing most messages. And comparing the minimum consumed time with the abnormal consumed time, wherein if the minimum consumed time is not less than the abnormal consumed time, the minimum consumed time indicates that the module consumes too long time for message processing, and a fault may exist. Wherein, the minimum time consumption can be expressed by a Top percentage index (i.e. TP index), a mean index, a median index, etc. Taking TP90 as an example, TP90 may be considered the minimum time consuming to ensure that 90% of requests are all responded to. For each module, the unmanned device can determine each monitoring data corresponding to the module according to the module identifier of the module, and determine the corresponding TP index according to each execution time in each monitoring data, as the standard execution time corresponding to the module.
S106: and when the module is determined to have a fault according to the determined confidence interval and the standard execution time of each module, sending prompt information for prompting that the module has the fault and needs to be optimized.
In one or more embodiments provided in this specification, after determining the confidence interval and the standard execution time of each module, the unmanned device may determine whether the module has a fault based on the determined confidence interval and the standard execution time of each module, and perform optimization based on the determination result.
Specifically, the unmanned device may determine, for each module, whether a standard execution time of the module is within a confidence interval corresponding to the module, if so, may determine that the module consumes normal time for processing the message, and if not, may determine that the module consumes excessively long time for processing the message and may have a fault, and may send a prompt message to a worker or other unmanned devices to prompt the module that the module has a fault and needs to be optimized.
Further, the unmanned device can determine, for each module, a maximum value of the confidence interval according to the confidence interval of the module, as the abnormal elapsed time corresponding to the module. The unmanned device can judge whether the standard execution time of the module exceeds the abnormal time consumption corresponding to the module, if so, the unmanned device can determine that the module is abnormal, and send prompt information based on the module identifier of the module.
Furthermore, in order to ensure the driving safety of the unmanned device and avoid the situation that the unmanned device has a fault but is not determined, the unmanned device further determines whether the consumed time of the module for processing the messages exceeds the abnormal consumed time corresponding to the module in the consumed time of each module. If so, the drone may send a prompt based on the message processing elapsed time.
In addition, in order to avoid faults occurring in the process of executing the distribution task by the unmanned equipment, in the running process of the unmanned equipment, abnormal consumed time corresponding to each module can be determined according to a preset time interval, whether the execution time of each module is too long is judged based on the determined abnormal consumed time, and if yes, prompt information can be sent so as to reduce the potential safety hazard of the unmanned equipment.
Further, there may be multiple versions of the system of the drone, and thus, for each module of the system execution message of the drone, the drone may preset an optimization threshold corresponding to each module according to a confidence interval corresponding to each module in the historical version of the drone. Therefore, the unmanned equipment can be optimized according to the determined abnormal time consumption of each module and a preset optimization threshold value. For example, the modules with abnormal time consumption higher than the optimization threshold are adjusted, the modules with abnormal time consumption lower than the optimization threshold are not adjusted temporarily, and the like.
It should be noted that the method for determining the optimization threshold may be determined by a person based on experience, and the method for optimizing the unmanned aerial vehicle according to the determined confidence interval of each module also does not only pass one of the optimization thresholds of each module, and the specific method for determining the optimization threshold and optimizing the unmanned aerial vehicle according to the confidence interval of each module may be set as needed, which is not limited in this specification.
Based on the system optimization method of the unmanned equipment provided by fig. 1, when the unmanned equipment executes a task, the consumed time of each module for processing each message is monitored and stored, after the unmanned equipment executes the task, the stored consumed time corresponding to each module is subjected to gaussian fitting, the processing consumed time distribution of each module is determined, when the processing consumed time distribution and a preset abnormal consumed time threshold value are used, a confidence interval corresponding to each module is determined, the standard execution time of each module is determined according to the consumed time of each module, and when the module is determined to have a fault according to the confidence interval and the standard execution time of each module, prompt information for prompting that the module has the fault and needs to be optimized is sent. The method comprises the steps of judging whether the time consumed for processing the messages by each module in the system of the unmanned equipment is too long or not through the time consumed for processing each message by each module, and sending prompt information for prompting the fault of the module if the time consumed for processing the messages by each module is too long. When the unmanned equipment has faults but does not show corresponding representations, the method can also determine the modules with the faults, and ensures the driving safety of the unmanned equipment.
Further, in one or more embodiments provided in this specification, if the processing time consumption distribution and the subsequent steps corresponding to each module are determined during the driving process of the unmanned aerial vehicle, and each module in the system is processing each message, it is obviously not appropriate to process the monitoring data. Therefore, in step S102, the unmanned aerial vehicle may further receive an optimization request, and then, according to the received optimization request and the stored consumed time for each module to process each message, the unmanned aerial vehicle may perform gaussian fitting on the consumed time for each module to process each message, and determine the consumed time for processing corresponding to each module.
In step S100, when each module of the system of the unmanned aerial vehicle is monitored, a code for starting the output of the "start processing time" and the "end processing time" may be added to the corresponding code module based on each module itself, in addition to the monitoring code embedded in advance. The drone may store each message based on its output time consumption for processing.
Further, in one or more embodiments provided in this specification, data stored by a system of the drone is in a non-volatile memory (e.g., a disk), and when the system of the drone processes a message, if the acquired time consumed by each module to process the message is stored in the disk, operations on the disk are increased, threads are occupied, and thus execution efficiency of the message is reduced. Therefore, in step S100, in order to avoid the influence on the message execution efficiency, when the time consumed by each module to process the message is obtained and stored, the message may be stored as a memory file. The memory file in the present specification refers to a file that is stored in a volatile memory (e.g., a memory) and does not affect the operation of the drone on a non-volatile memory (e.g., a disk). For example, the monitoring data is stored in volatile memory as a temporary file system (tmpfs) file. Volatile memories generally include Static Random Access Memories (SRAMs), Dynamic Random Access Memories (DRAMs), and the like.
Furthermore, the memory of the drone is limited, and when a large number of memory files exist in the memory, the efficiency of processing messages by the system of the drone may be affected, which may cause a safety hazard. Therefore, in step S100, in order not to affect the execution efficiency of the message, the unmanned aerial vehicle may further store the generated memory file in the disk according to a preset storage condition. The storage condition may be to store the memory file according to a preset time interval, or to store the time consumed by processing the message by each module corresponding to the message after the message is executed. The specific storage conditions can be set as required, and this specification does not limit this.
In addition, in step S108, it may also be determined whether the module has a fault only according to the standard execution time of each module, specifically, the execution time threshold of each module may be determined according to the historical standard execution time corresponding to each module, and when the standard execution time of each module exceeds the corresponding execution time threshold, the prompt message may be sent.
Based on the same idea, the present specification further provides a corresponding system optimization apparatus for an unmanned aerial vehicle, as shown in fig. 4.
Fig. 4 is a schematic diagram of a system optimization device of the unmanned aerial vehicle provided by the present specification, including:
an obtaining module 200, configured to monitor, for each module in the system, time consumed by the module for performing message processing each time when the unmanned device executes a task, and store the time consumed.
A first determining module 202, configured to perform gaussian fitting on the stored time consumed by each module when processing each message after the unmanned device executes the task, and determine processing time consumption distributions corresponding to each module.
The second determining module 204 is configured to determine, according to a preset abnormal consumed time threshold, a confidence interval corresponding to the abnormal consumed time threshold in each processing consumed time distribution, and determine, according to consumed time of each module processing each message, a standard execution time corresponding to each module.
And the prompting module 206 is configured to send a prompting message for prompting that the module has a fault and needs to be optimized when the module has the fault according to the determined confidence interval and the standard execution time of each module.
Optionally, the second determining module 204 is configured to determine, for each module, a mean and a variance corresponding to the processing time consumption distribution corresponding to the module according to the processing time consumption distribution corresponding to the module, and determine a confidence interval of the processing time consumption distribution according to the mean, the variance, and a preset abnormal time consumption threshold.
Optionally, the prompting module 206 is configured to determine, for each module, an abnormal consumed time corresponding to the module according to the confidence interval, determine that the module has a fault when the standard execution time is not less than the abnormal consumed time, and send, according to the module identifier of the module, a prompting message for prompting that the module has the fault and needs to be optimized.
Optionally, the obtaining module 200 is configured to receive an optimization request when the unmanned aerial vehicle executes a task, determine time consumed for the module to process each message according to a module identifier carried in the optimization request, and determine processing time consumption distribution of the module according to the time consumed for the module to process each message.
Optionally, the prompt module 206 is configured to store time consumed by each module for processing each message according to the message identifier of each message, determine, for each module, abnormal time consumed by the module for processing the message according to the confidence interval, determine, when the module has a fault, a message whose message processing time is not less than the abnormal time consumed, serve as the abnormal message, and send the prompt information according to the determined abnormal message.
Optionally, the obtaining module 200 is configured to obtain time consumed by the module when the module performs message processing each time through a monitoring code corresponding to the module, which is embedded in the system in advance for the module.
Optionally, the system in the unmanned device has multiple versions, and the prompt module is configured to determine historical abnormal consumed time corresponding to each module according to the abnormal consumed time corresponding to each module of the historical version of the system, determine abnormal consumed time corresponding to each module according to a confidence interval corresponding to each determined module, and send prompt information when the abnormal consumed time of each module is not less than the historical abnormal consumed time.
The present specification also provides a computer-readable storage medium having stored thereon a computer program operable to execute the method for system optimization of an unmanned aerial device as provided in fig. 1 above.
This specification also provides a schematic block diagram of the electronic device shown in fig. 5. As shown in fig. 5, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, but may also include hardware required for other services. The processor reads a corresponding computer program from the non-volatile memory into the memory and then runs the computer program to implement the system optimization method of the unmanned aerial vehicle described in fig. 1. Of course, besides the software implementation, the present specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may be hardware or logic devices.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, 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, 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.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (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 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.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus 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 apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
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 an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description 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.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.
Claims (10)
1. A method for optimizing a system of an unmanned aerial vehicle, the system comprising a plurality of modules, comprising:
when the unmanned equipment executes a task, monitoring the consumed time of each module in the system when the module processes the message each time, and storing the consumed time;
after the unmanned equipment completes the task, performing Gaussian fitting on the stored time consumed by each module when processing each message, and determining the processing time consumption distribution corresponding to each module;
determining a confidence interval of each processing time consumption distribution corresponding to the abnormal time consumption threshold according to a preset abnormal time consumption threshold, and determining a standard execution time corresponding to each module according to the time consumption of each module for processing each message;
and when the module is determined to have a fault according to the determined confidence interval and the standard execution time of each module, sending prompt information for prompting that the module has the fault and needs to be optimized.
2. The method according to claim 1, wherein determining a confidence interval of each processing elapsed time distribution corresponding to the abnormal elapsed time threshold according to a preset abnormal elapsed time threshold specifically comprises:
for each module, determining a mean value and a variance corresponding to the processing time consumption distribution corresponding to the module according to the processing time consumption distribution corresponding to the module;
and determining a confidence interval of the processing time consumption distribution according to the mean value, the variance and a preset abnormal time consumption threshold.
3. The method of claim 2, wherein when determining that the module has a fault according to the determined confidence interval and the standard execution time of each module, sending a prompt message for prompting that the module has the fault and needs to be optimized includes:
for each module, determining the corresponding abnormal time consumption of the module according to the confidence interval;
when the standard execution time is not less than the abnormal time consumption, determining that the module has a fault;
and sending prompt information for prompting that the module has a fault and needs to be optimized according to the module identifier of the module.
4. The method of claim 1, wherein the method further comprises:
receiving an optimization request while the unmanned device is performing a task;
determining the time consumed by the module for processing each message according to the module identifier carried in the optimization request;
and determining the processing time consumption distribution of the module according to the time consumption of the module for processing each message.
5. The method of claim 1, wherein the method further comprises:
storing the time consumed by each module for processing each message according to the message identifier of each message;
for each module, determining the abnormal time consumption of the module for processing the message according to the confidence interval, and determining the message with the time consumption not less than the abnormal time consumption as the abnormal message when the module has a fault;
and sending prompt information according to the determined abnormal message.
6. The method of claim 1, wherein monitoring the time consumed by the module each time a message is processed comprises:
and acquiring the time consumption of the module in each message processing by aiming at the monitoring code corresponding to the module embedded in the system in advance.
7. The method of claim 1, wherein there are multiple versions of a system in the drone, the method further comprising:
determining historical abnormal time consumption corresponding to each module according to the abnormal time consumption corresponding to each module of the historical version of the system;
determining the abnormal time consumption corresponding to each module according to the determined confidence interval corresponding to each module;
and when the abnormal time consumption of each module is not less than the historical abnormal time consumption, sending prompt information.
8. An apparatus for optimizing a system of an unmanned aerial device, comprising:
the acquisition module is used for monitoring the consumed time of each module in the system when the unmanned equipment executes a task and storing the consumed time;
the first determining module is used for performing Gaussian fitting on the stored time consumed by each module when processing each message after the unmanned equipment executes the task, and determining the processing time consumption distribution corresponding to each module;
the second determining module is used for determining a confidence interval of each processing time consumption distribution corresponding to the abnormal time consumption threshold according to a preset abnormal time consumption threshold, and determining standard execution time corresponding to each module according to the time consumption of each module in processing each message;
and the prompting module is used for sending prompting information for prompting that the module has the fault and needs to be optimized when the module has the fault according to the determined confidence interval and the standard execution time of each module.
9. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 7 when executing the program.
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