CN116704822A - Big data-based communication processing method and system - Google Patents
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Abstract
The application discloses a communication processing method and a system based on big data, in particular relates to the technical field of communication data processing, and is used for solving the problems of incomplete and accurate communication data acquired during working when the communication quality of the existing unmanned aerial vehicle is poor; the system comprises a data processing module, an information acquisition module, a signal strength evaluation module and a signal quality judgment module, wherein the information acquisition module, the signal strength evaluation module and the signal quality judgment module are in communication connection with the data processing module; the communication condition of the unmanned aerial vehicle can be known more accurately and measures can be taken pertinently by collecting the communication information, the external environment information and the signal intensity information of the unmanned aerial vehicle and comprehensively analyzing and calculating the data to obtain the communication quality evaluation coefficient and evaluating and marking the communication quality; communication interruption and data loss are reduced, and the success rate and reliability of task execution are improved; providing a reliable basis for subsequent data analysis and decision making.
Description
Technical Field
The application relates to the technical field of communication data processing, in particular to a communication processing method and system based on big data.
Background
Unmanned aerial vehicles (Unmanned Aerial Vehicle, abbreviated as UAVs) refer to unmanned aerial vehicles that are commonly operated by remote controls, automated systems, or preset airlines. Unmanned aerial vehicles can perform various tasks and applications, and have wide application and potential benefits; some common unmanned aerial vehicle applications include geographic mapping and measurement, agriculture and forestry, building and infrastructure inspection, scientific research, and environmental monitoring.
The communication processing of the unmanned aerial vehicle comprises the steps of processing communication data generated by the unmanned aerial vehicle during working; the existing unmanned aerial vehicle needs to transmit a large amount of communication data, such as images, videos, sensor data and the like, when working; the prior art lacks the real-time judgement to unmanned aerial vehicle communication quality not enough comprehensively, causes the unmanned aerial vehicle to be when communication quality is poor easily, and the communication data that the during operation obtained is not enough complete and accurate, and data acquisition's accuracy becomes low, influences work efficiency.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present application provide a method and a system for processing communication based on big data, so as to solve the above-mentioned problems in the prior art.
In order to achieve the above purpose, the present application provides the following technical solutions:
the communication processing method based on big data comprises the following steps:
step S1: collecting signal intensity information, and calculating to obtain a signal evaluation value according to the signal intensity information;
step S2: setting a signal evaluation threshold, judging the signal strength condition according to the comparison of the signal evaluation value and the signal evaluation threshold, and marking a monitoring area;
step S3: acquiring unmanned aerial vehicle communication information and external environment information, and calculating a communication quality evaluation coefficient according to the signal strength information unmanned aerial vehicle communication information and the external environment information;
step S4: and judging the communication quality condition of the unmanned aerial vehicle in each monitoring area by comparing the communication quality evaluation coefficient with a communication evaluation threshold value, and making measures for the monitoring area with poor communication quality.
In a preferred embodiment, in step S1, the total time of the unmanned aerial vehicle task is marked as T, T is divided into a plurality of working time intervals with equal time, the working time intervals are marked as T, and the corresponding working area of the unmanned aerial vehicle in each T is marked as a monitoring area;
the signal intensity information is reflected by the signal evaluation value; the acquisition logic of the signal evaluation value is as follows:
the path loss is calculated by the expression: pa=20log10 (f×d); pa is path loss, and f is working frequency; d is the transmission distance; acquiring the number of times of signal interruption of the unmanned aerial vehicle in t, and calculating an interruption ratio, wherein the expression is as follows:zd is the interrupt ratio, zc is the number of times of signal interrupt of the unmanned aerial vehicle in t;
calculating a signal evaluation value in t, wherein the expression is as follows:xp is a signal evaluation value, < >>Is the average of the path loss within t.
In a preferred embodiment, in step S2, a signal evaluation threshold is set; when the signal evaluation value is larger than the signal evaluation threshold value, generating a signal poor signal, and marking a monitoring area corresponding to the signal poor signal in the t as an area to be monitored again; and when the signal evaluation value is larger than the signal evaluation threshold value, generating a signal normal signal, and marking a corresponding monitoring area in the t as a signal normal area.
In a preferred embodiment, in step S3, the monitoring area marked as the area to be monitored again is screened out; collecting unmanned aerial vehicle communication information, wherein the unmanned aerial vehicle communication information comprises delay information and spectrum congestion information;
the delay information is collected and embodied by a delay evaluation value, and the delay evaluation value acquisition logic is as follows:
calculating the number of times of round trip delay, calculating the number of times that the round trip delay is larger than the round trip delay threshold value in t, and calculating the average value of the round trip delay in t; the delay evaluation value expression is:wherein Yp, cy, wc, cp are delay evaluation values respectivelyThe number of times the round trip delay is greater than the round trip delay threshold, the number of times the round trip delay is, and the average value of the round trip delay in t;
collecting spectrum congestion information, wherein the spectrum congestion information comprises a spectrum utilization rate and a channel conflict rate; the spectrum utilization rate is the ratio of the used spectrum bandwidth to the total bandwidth of the frequency band within t; the channel conflict rate is the ratio of the number of conflict channels to the total number of channels in t;
the external environment information is reflected by weather influence values; acquiring the number of times of knocking the unmanned aerial vehicle by raindrops in t based on a raindrop sensor; the weather effect value is the ratio of the number of times the unmanned aerial vehicle is knocked by raindrops to t in t.
In a preferred embodiment, the signal evaluation value, the delay evaluation value, the spectrum utilization rate, the channel collision rate, and the weather influence value are subjected to normalization processing to calculate the communication quality evaluation coefficient, the expression of which is:TP is a communication quality evaluation coefficient, and Pl, XL and Ty are respectively a spectrum utilization rate, a channel conflict rate and a weather influence value; alpha 1 、α 2 、α 3 、α 4 、α 5 Preset scaling factors of signal evaluation value, delay evaluation value, spectrum utilization rate, channel conflict rate and weather influence value, respectively, and alpha 1 、α 2 、α 3 、α 4 、α 5 Are all greater than 0.
In a preferred embodiment, in step S4, a communication evaluation threshold is set; calculating a communication quality evaluation coefficient of a monitoring area corresponding to each t;
when the communication quality evaluation coefficient is smaller than or equal to the communication evaluation threshold value, marking the monitoring area as normal comprehensive communication; and when the communication quality evaluation coefficient is larger than the communication evaluation threshold value, marking the monitoring area as poor comprehensive communication.
In a preferred embodiment, the big data-based communication processing system comprises a data processing module, an information acquisition module, a signal strength evaluation module and a signal quality judgment module, wherein the information acquisition module, the signal strength evaluation module and the signal quality judgment module are in communication connection with the data processing module;
the information acquisition module acquires signal intensity information, the signal intensity information is sent to the data processing module, and the data processing module calculates a signal evaluation value;
the information acquisition module acquires unmanned aerial vehicle communication information and external environment information, and sends the signal strength information, the unmanned aerial vehicle communication information and the external environment information to the data processing module, and the data processing module calculates a communication quality evaluation coefficient;
the signal strength evaluation module marks the monitoring area according to the comparison of the signal evaluation value and the signal evaluation threshold value;
and the signal quality judging module judges the communication quality condition of the unmanned aerial vehicle in each monitoring area according to the comparison between the communication quality evaluation coefficient and the communication evaluation threshold value.
The communication processing method and the communication processing system based on big data have the technical effects and advantages that:
1. the signal evaluation value is calculated to measure the signal strength and the reliability through the path loss, the interrupt ratio and other parameters, the monitoring area is marked according to the signal evaluation value and the set signal evaluation threshold value, and the communication condition of the unmanned aerial vehicle can be more accurately known through marking the monitoring area, so that the communication efficiency is improved, the communication interrupt and the data loss are reduced, and the success rate and the reliability of task execution are improved.
2. The communication quality evaluation coefficient is obtained by collecting unmanned aerial vehicle communication information, external environment information and signal intensity information and comprehensively analyzing and calculating the data, and can be used for evaluating the communication quality and the data collection condition of each monitoring area; by evaluating and marking the communication quality, the communication condition of the unmanned aerial vehicle can be known more accurately, and measures can be taken in a targeted manner; communication interruption and data loss are reduced, and the success rate and reliability of task execution are improved; providing a reliable basis for subsequent data analysis and decision-making; by processing and analyzing a large amount of communication data, the communication quality of the unmanned aerial vehicle can be estimated, and measures can be timely taken to improve the communication quality so that the unmanned aerial vehicle can acquire complete and accurate communication data.
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FIG. 1 is a schematic diagram of a big data based communication processing method of the present application;
fig. 2 is a schematic structural diagram of a big data based communication processing system according to the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
Fig. 1 shows a schematic diagram of a communication processing method based on big data, which includes the following steps:
step S1: and acquiring signal intensity information, and calculating to obtain a signal evaluation value according to the signal intensity information.
Step S2: and setting a signal evaluation threshold, judging the signal strength condition according to the comparison of the signal evaluation value and the signal evaluation threshold, and marking the monitoring area.
Step S3: and acquiring the unmanned aerial vehicle communication information and the external environment information, and calculating a communication quality evaluation coefficient according to the signal strength information unmanned aerial vehicle communication information and the external environment information.
Step S4: and judging the communication quality condition of the unmanned aerial vehicle in each monitoring area by comparing the communication quality evaluation coefficient with a communication evaluation threshold value, and making measures for the monitoring area with poor communication quality.
In step S1, the total time of the unmanned aerial vehicle task is marked as T, T is divided into a plurality of working time intervals with equal time, the working time intervals are marked as T, t=n×t, and n is a positive integer greater than 1; and marking the corresponding working area of each unmanned aerial vehicle in t as a monitoring area.
Firstly, collecting signal intensity information, wherein the signal intensity is an important index in unmanned aerial vehicle communication data and is used for evaluating communication quality and judging signal coverage; the signal strength information is represented by a signal evaluation value.
The signal strength of the drone is related to a plurality of factors including transmission power, antenna gain, transmission distance, propagation environment, etc., which may vary from one communication system to another and from one device to another; and comprehensively evaluating the signal strength of the unmanned aerial vehicle by acquiring the working frequency, the transmission distance and the signal interruption condition of the unmanned aerial vehicle.
The acquisition logic of the signal evaluation value is as follows:
the path loss is calculated by the expression: pa=20log10 (f×d); wherein Pa is path loss, and f is working frequency (unit: hertz); d is the transmission distance (in meters); the larger the path loss is, the lower the signal strength is, which means that the signal of the unmanned aerial vehicle experiences larger attenuation and loss in the transmission process, and the degree of the signal strength attenuation is higher; the reliability and stability of the signal transmission is affected.
The transmission distance is the physical distance from the transmitting end to the receiving end of the signal, namely the physical distance between the unmanned aerial vehicle control end and the unmanned aerial vehicle body; the operating frequency refers to the signal transmission frequency of unmanned aerial vehicle communication.
Acquiring the number of times of signal interruption of the unmanned aerial vehicle in t, and calculating an interruption ratio, wherein the expression is as follows:wherein Zd is the interrupt ratio, zc is the number of unmanned aerial vehicle signal interrupt times in t.
Calculating a signal evaluation value in t, wherein the expression is as follows:wherein Xp is a signal evaluation value, +.>Is the average of the path loss within t.
The larger the signal evaluation value is, the lower the signal intensity of the unmanned aerial vehicle in t is, the larger the path loss is and the higher the interruption ratio is, which means that the signal intensity of the unmanned aerial vehicle in the time period is lower and the interruption frequency is higher.
In step S2, a signal evaluation threshold is set, and the monitoring area is marked by comparing the signal evaluation value with the signal evaluation threshold.
When the signal evaluation value is larger than the signal evaluation threshold value, the path loss in t is large, the interruption frequency is higher, and the signal strength is poor; generating a signal poor signal, and marking the corresponding monitoring area in the t as an area to be monitored again.
When the signal evaluation value is larger than the signal evaluation threshold value, the signal strength of the unmanned aerial vehicle is normal in t; generating a signal normal signal, and marking the corresponding monitoring area in the t as a signal normal area.
The signal evaluation threshold is set according to the magnitude of the signal evaluation value, T, t, and other practical situations, which will not be described herein.
The signal evaluation value is calculated to measure the signal strength and the reliability through the path loss, the interrupt ratio and other parameters, the monitoring area is marked according to the signal evaluation value and the set signal evaluation threshold value, and the communication condition of the unmanned aerial vehicle can be more accurately known through marking the monitoring area, so that the communication efficiency is improved, the communication interrupt and the data loss are reduced, and the success rate and the reliability of task execution are improved.
In step S3, the marked condition of each monitoring area is obtained, and the monitoring area marked as the area to be monitored again is screened out; and the monitoring area marked as the signal normal area is further analyzed by collecting the communication information and the external environment information of the unmanned aerial vehicle and combining the signal intensity information, so that whether the data acquisition of the unmanned aerial vehicle is accurate and complete for the monitoring area generating the signal normal signal is judged.
Unmanned aerial vehicle communication information is acquired, and the unmanned aerial vehicle communication information comprises delay information and spectrum congestion information.
The delay information is collected and embodied by a delay evaluation value, and the delay evaluation value acquisition logic is as follows:
the process of sending data from a sending end to an unmanned aerial vehicle and returning the data to the sending end is called round-trip delay, namely the complete round-trip transmission time of sending the data from the sending end to the unmanned aerial vehicle and returning the data from the unmanned aerial vehicle to the sending end; the number of round trip delays is calculated, the number of times the round trip delay is greater than the round trip delay threshold value within t is calculated, and the average value of the round trip delay within t is calculated.
The round trip delay threshold is set according to the actual situations such as the round trip delay and the influence of the delay on the unmanned aerial vehicle, and the like, and will not be described here.
The delay evaluation value expression is:the delay evaluation value, the number of times the round trip delay is greater than the round trip delay threshold value within t, the number of times the round trip delay, and the average value of the round trip delay within t are shown as Yp, cy, wc, cp.
The larger the delay evaluation value, the more frequent the round trip delay is in t, and the more delay is in the whole, the more adverse effect on the communication quality of the unmanned aerial vehicle is.
Spectrum congestion information is collected, and the spectrum congestion information comprises spectrum utilization rate and channel conflict rate.
The spectrum utilization rate is a proportional relation between the number of unmanned aerial vehicles or other communication equipment used simultaneously on a given frequency band and the total capacity of the frequency band; the spectrum utilization is the ratio of the used spectrum bandwidth to the total bandwidth of the frequency band within t.
The channel conflict rate refers to the proportion of the channels with conflicts in the communication process to the total communication channel quantity; the channel collision rate is the ratio of the number of collision channels to the total number of channels in t.
The high frequency spectrum utilization rate indicates that more communication devices on the frequency band possibly cause frequency spectrum congestion, so that the communication quality and the data transmission rate of the unmanned aerial vehicle are affected; the high channel conflict rate means that under the condition of spectrum congestion, frequent conflict occurs between the unmanned aerial vehicle and other devices, so that errors and retransmission of data transmission are caused, and the communication quality and stability are affected.
Spectrum bandwidth has been used: the used spectrum bandwidth may be monitored and recorded by a spectrum monitoring instrument or radio spectrum analysis software.
Total bandwidth of frequency band: the total bandwidth of the frequency band may be obtained by an associated spectrum management authority or an associated technical specification.
Number of collision channels: can be obtained by detecting and recording the number of channels in conflict during communication; it can also be realized by collision detection algorithm, collision report mechanism, etc.
Total channel number: the total number of channels may be determined according to the design and specifications of the communication system, for example, in wireless communication, the total number of channels may be the number of available frequencies.
The specific acquisition method and implementation may vary from application scenario to application scenario and implementation to technology. In practice, it may be desirable to incorporate appropriate sensors, monitoring devices, signal analysis tools, etc. to collect and analyze relevant data.
And collecting external environment information, wherein the external environment information is reflected by weather influence values.
In a weather environment, the influence of rainfall on unmanned aerial vehicle communication data is large, and the rainfall can cause signal attenuation, multipath propagation and scattering, so that the signal strength is reduced, the transmission distance is shortened, and the error rate of signal transmission is increased; the water droplets have an absorbing and scattering effect on the radio signal, which can cause the communication signal to be disturbed and attenuated; therefore, in rainy weather, the communication quality of the unmanned aerial vehicle may be most affected.
The raindrop sensor is a sensing device and is mainly used for detecting whether raining and the magnitude of rainfall are performed; acquiring the number of times of knocking the unmanned aerial vehicle by raindrops in t based on a raindrop sensor; the weather effect value is the ratio of the number of times the unmanned aerial vehicle is knocked by raindrops to t in t; the larger the weather effect value is, the more serious the rainfall condition in t is, namely the worse the weather condition is, the more serious the communication quality of the unmanned aerial vehicle is affected.
The signal evaluation value, the delay evaluation value, the frequency spectrum utilization rate, the channel conflict rate and the weather influence value are subjected to normalization processing, and a communication quality evaluation coefficient is calculated, wherein the expression is as follows:TP is a communication quality evaluation coefficient, and Pl, XL and Ty are respectively a spectrum utilization rate, a channel conflict rate and a weather influence value; alpha 1 、α 2 、α 3 、α 4 、α 5 Preset scaling factors of signal evaluation value, delay evaluation value, spectrum utilization rate, channel conflict rate and weather influence value, respectively, and alpha 1 、α 2 、α 3 、α 4 、α 5 Are all greater than 0.
The larger the communication quality evaluation coefficient is, the worse the communication quality of the unmanned aerial vehicle in t is, and the worse the accuracy and the integrity of data acquisition of a monitoring area corresponding to the unmanned aerial vehicle in t are.
In step S4, a communication evaluation threshold is set, and the communication quality of the unmanned aerial vehicle in each monitoring area is determined by comparing the communication quality evaluation coefficient with the communication evaluation threshold.
And calculating the communication quality evaluation coefficient of the monitoring area corresponding to each t.
When the communication quality evaluation coefficient is smaller than or equal to the communication evaluation threshold, the communication quality of the unmanned aerial vehicle in the monitoring area is normal, the accuracy and the integrity of data acquisition are good, and the monitoring area is marked as the integrated communication is normal.
When the communication quality evaluation coefficient is larger than the communication evaluation threshold, the communication quality of the unmanned aerial vehicle in the monitoring area is poor, the accuracy and the integrity of data acquisition are poor, and the monitoring area is marked as poor comprehensive communication.
The communication evaluation threshold is set according to the actual situations such as the magnitude of the communication quality evaluation coefficient and the standard of the quality of the communication, and will not be described here.
For the monitoring area marked as the area to be monitored again and the monitoring area marked as the area with poor comprehensive communication, namely the monitoring area with poor communication quality, the monitoring area needs to be re-operated to acquire accurate and complete data.
And (3) making measures for a monitoring area with poor communication quality:
after the unmanned aerial vehicle finishes the task in T, acquiring a monitoring area marked as an area to be monitored again and a monitoring area marked as a monitoring area with poor comprehensive communication; firstly, a professional technician is arranged to inspect the unmanned aerial vehicle, and the unmanned aerial vehicle is classified into poor state and normal state. Evaluating the environment of the monitored area: the professional technician is arranged to evaluate the monitoring area environment, including but not limited to forest shelter, elevation, terrain and buildings and electromagnetic interference sources, which is classified as poor and normal.
Measure one: if the unmanned aerial vehicle is in a poor state, namely, the hardware and the software are in a problem, the unmanned aerial vehicle is maintained.
And a second measure: if the unmanned aerial vehicle is in a poor state and the environment of the monitoring area is not good, re-formulating the unmanned aerial vehicle flight strategy; if the unmanned aerial vehicle is in a normal state but the monitoring area environment is poor, the unmanned aerial vehicle flight strategy is formulated again.
And step three: if the unmanned aerial vehicle is not in good state and the environment of the monitoring area is normal, the unmanned aerial vehicle with normal communication state is arranged to work on the monitoring area again.
And a fourth measure: if the unmanned aerial vehicle is in a normal state and the environment of the monitoring area is normal, the unmanned aerial vehicle is arranged to work again on the monitoring area.
The judgment of the monitoring area environment and the evaluation of the unmanned aerial vehicle state are mature technologies in the prior art, and are not repeated here.
The communication quality evaluation coefficient is obtained by collecting unmanned aerial vehicle communication information, external environment information and signal intensity information and comprehensively analyzing and calculating the data, and can be used for evaluating the communication quality and the data collection condition of each monitoring area; by evaluating and marking the communication quality, the communication condition of the unmanned aerial vehicle can be known more accurately, and measures can be taken in a targeted manner; communication interruption and data loss are reduced, and the success rate and reliability of task execution are improved; under the condition of poor communication quality, measures can be timely taken for improvement, and the risks of communication faults and data loss are reduced, so that the real-time performance and reliability of monitoring and data acquisition are improved, and a reliable basis is provided for subsequent data analysis and decision.
Example 2
Embodiment 2 of the present application differs from embodiment 1 in that this embodiment is described in terms of a large data-based communication processing system.
Fig. 2 is a schematic structural diagram of the big data-based communication processing system of the present application, which includes a data processing module, and an information acquisition module, a signal strength evaluation module, and a signal quality judgment module communicatively connected to the data processing module.
The information acquisition module acquires signal intensity information, the signal intensity information is sent to the data processing module, and the data processing module calculates a signal evaluation value.
The information acquisition module acquires unmanned aerial vehicle communication information and external environment information, sends the signal strength information, the unmanned aerial vehicle communication information and the external environment information to the data processing module, and the data processing module calculates a communication quality evaluation coefficient.
The signal strength evaluation module marks the monitoring area according to the comparison of the signal evaluation value and the signal evaluation threshold value.
And the signal quality judging module judges the communication quality condition of the unmanned aerial vehicle in each monitoring area according to the comparison between the communication quality evaluation coefficient and the communication evaluation threshold value.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system, apparatus and module may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (7)
1. The communication processing method based on big data is characterized by comprising the following steps:
step S1: collecting signal intensity information, and calculating to obtain a signal evaluation value according to the signal intensity information;
step S2: setting a signal evaluation threshold, judging the signal strength condition according to the comparison of the signal evaluation value and the signal evaluation threshold, and marking a monitoring area;
step S3: acquiring unmanned aerial vehicle communication information and external environment information, and calculating a communication quality evaluation coefficient according to the signal strength information unmanned aerial vehicle communication information and the external environment information;
step S4: and judging the communication quality condition of the unmanned aerial vehicle in each monitoring area by comparing the communication quality evaluation coefficient with a communication evaluation threshold value, and making measures for the monitoring area with poor communication quality.
2. The big data based communication processing method according to claim 1, wherein: in step S1, marking the total time of the unmanned aerial vehicle task as T, dividing the T into a plurality of working time intervals with equal time, marking the working time intervals as T, and marking the corresponding working areas of the unmanned aerial vehicle in each T as monitoring areas;
the signal intensity information is reflected by the signal evaluation value; the acquisition logic of the signal evaluation value is as follows:
the path loss is calculated by the expression: pa=20log10 (f×d); pa is path loss, and f is working frequency; d is the transmission distance; acquiring the number of times of signal interruption of the unmanned aerial vehicle in t, and calculating an interruption ratio, wherein the expression is as follows:zd is the interrupt ratio, zc is the number of times of signal interrupt of the unmanned aerial vehicle in t;
calculating a signal evaluation value in t, wherein the expression is as follows:xp is a signal evaluation value, < >>Is the average of the path loss within t.
3. The big data based communication processing method according to claim 2, wherein: in step S2, a signal evaluation threshold is set; when the signal evaluation value is larger than the signal evaluation threshold value, generating a signal poor signal, and marking a monitoring area corresponding to the signal poor signal in the t as an area to be monitored again; and when the signal evaluation value is larger than the signal evaluation threshold value, generating a signal normal signal, and marking a corresponding monitoring area in the t as a signal normal area.
4. A big data based communication processing method according to claim 3, wherein: in step S3, the monitoring area marked as the area to be monitored again is screened out; collecting unmanned aerial vehicle communication information, wherein the unmanned aerial vehicle communication information comprises delay information and spectrum congestion information;
the delay information is collected and embodied by a delay evaluation value, and the delay evaluation value acquisition logic is as follows:
calculating the number of times of round trip delay, calculating the number of times that the round trip delay is larger than the round trip delay threshold value in t, and calculating the average value of the round trip delay in t; the delay evaluation value expression is:wherein Yp, cy, wc, cp is the delay evaluation value, the number of times the round trip delay is greater than the round trip delay threshold value in t, the number of times the round trip delay, and the average value of the round trip delay in t, respectively;
collecting spectrum congestion information, wherein the spectrum congestion information comprises a spectrum utilization rate and a channel conflict rate; the spectrum utilization rate is the ratio of the used spectrum bandwidth to the total bandwidth of the frequency band within t; the channel conflict rate is the ratio of the number of conflict channels to the total number of channels in t;
the external environment information is reflected by weather influence values; acquiring the number of times of knocking the unmanned aerial vehicle by raindrops in t based on a raindrop sensor; the weather effect value is the ratio of the number of times the unmanned aerial vehicle is knocked by raindrops to t in t.
5. The big data based communication processing method according to claim 4, wherein: the signal evaluation value, the delay evaluation value, the frequency spectrum utilization rate, the channel conflict rate and the weather influence value are subjected to normalization processing, and a communication quality evaluation coefficient is calculated, wherein the expression is as follows:TP is a communication quality evaluation coefficient, and Pl, XL and Ty are respectively a spectrum utilization rate, a channel conflict rate and a weather influence value; alpha 1 、α 2 、α 3 、α 4 、α 5 Preset scaling factors of signal evaluation value, delay evaluation value, spectrum utilization rate, channel conflict rate and weather influence value, respectively, and alpha 1 、α 2 、α 3 、α 4 、α 5 Are all greater than 0.
6. The big data based communication processing method according to claim 5, wherein: in step S4, a communication evaluation threshold is set; calculating a communication quality evaluation coefficient of a monitoring area corresponding to each t;
when the communication quality evaluation coefficient is smaller than or equal to the communication evaluation threshold value, marking the monitoring area as normal comprehensive communication; and when the communication quality evaluation coefficient is larger than the communication evaluation threshold value, marking the monitoring area as poor comprehensive communication.
7. A big data based communication processing system for implementing the big data based communication processing method of any of claims 1-6, characterized by: the system comprises a data processing module, an information acquisition module, a signal strength evaluation module and a signal quality judgment module, wherein the information acquisition module, the signal strength evaluation module and the signal quality judgment module are in communication connection with the data processing module;
the information acquisition module acquires signal intensity information, the signal intensity information is sent to the data processing module, and the data processing module calculates a signal evaluation value;
the information acquisition module acquires unmanned aerial vehicle communication information and external environment information, and sends the signal strength information, the unmanned aerial vehicle communication information and the external environment information to the data processing module, and the data processing module calculates a communication quality evaluation coefficient;
the signal strength evaluation module marks the monitoring area according to the comparison of the signal evaluation value and the signal evaluation threshold value;
and the signal quality judging module judges the communication quality condition of the unmanned aerial vehicle in each monitoring area according to the comparison between the communication quality evaluation coefficient and the communication evaluation threshold value.
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