Unmanned aircraft state monitoring method and system for cloud computer and medium

Abstract

According to the unmanned aerial vehicle state monitoring method and system for the cloud computer and the medium, the flight state of the unmanned aerial vehicle can be monitored in real time through connection with the cloud computer system, accurate flight parameter data are provided, and the unmanned aerial vehicle state can be monitored through calculation and storage capacity of the cloud computer. A large amount of flight parameter data can be efficiently processed and analyzed, the cloud computer system can store the flight parameter data of the unmanned aerial vehicle to form historical data records, the data can be used for follow-up backtracking analysis, troubleshooting and flight mode optimization, and by means of utilization of the historical data, the unmanned aerial vehicle can be optimized. And the flight safety, reliability and efficiency of the unmanned aircraft can be further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and more specifically, to a method, system, and medium for monitoring the state of an unmanned aerial vehicle for a cloud computer. Background Art

[0002] With the rapid development and wide application of unmanned aerial vehicles, the accurate monitoring and management of the flight state of unmanned aerial vehicles have become increasingly important. Traditional unmanned aerial vehicle flight monitoring methods mainly rely on local hardware devices and manual operations, and have problems such as limited monitoring range, poor real-time performance, and human errors. The rapid development of cloud computer technology provides a new solution for the state monitoring of unmanned aerial vehicles. Through high-speed network connections and remote access, unmanned aerial vehicles can communicate and transmit data with the cloud in real time. By real-time monitoring and evaluating flight parameters, abnormal situations, violations, or flight risks can be detected in a timely manner, and corresponding measures can be taken. In addition, cloud computers also provide the ability to store and perform retrospective analysis. The flight parameter data of unmanned aerial vehicles can be stored in a cloud server for subsequent data analysis, troubleshooting, and optimization of the ideal flight state mode.

[0003] In summary, cloud computer technology provides more powerful data processing and storage capabilities for the state monitoring of unmanned aerial vehicles, realizing the functions of real-time monitoring, warning, and data analysis. It has broad application prospects in improving the flight safety, management efficiency, and operation convenience of unmanned aerial vehicles. Summary of the Invention

[0004] In view of the above problems, the purpose of the present invention is to provide a method, system, and medium for monitoring the state of an unmanned aerial vehicle for a cloud computer, which improve the flight safety, management efficiency, and operation convenience of unmanned aerial vehicles.

[0005] The first aspect of the present invention provides a method for monitoring the state of an unmanned aerial vehicle for a cloud computer, including:

[0006] Obtaining the real-time flight state parameters of the unmanned aerial vehicle;

[0007] Comparing and analyzing the real-time flight state parameters of the unmanned aerial vehicle with the preset ideal flight state parameters to obtain the deviation value of the flight state parameters;

[0008] Judging whether the deviation value of the flight state parameters of the unmanned aerial vehicle exceeds the preset deviation threshold. If so, generating the flight revision parameters of the unmanned aerial vehicle and sending the flight revision parameters of the unmanned aerial vehicle to the corresponding unmanned aerial vehicle for flight adjustment; if not, displaying that the flight of the unmanned aerial vehicle is normal on the cloud computer side.

[0009] Extract the characteristics of the flight state parameters of the unmanned aerial vehicle;

[0010] Query in the preset characteristic drag coefficient table according to the characteristics of the flight state parameters of the unmanned aerial vehicle to obtain the drag coefficient of the flight state parameters of the unmanned aerial vehicle corresponding to the characteristics during the current flight;

[0011] Divide the deviation value of the flight state parameters of the unmanned aerial vehicle by the corresponding drag coefficient to obtain the current flight state revision parameter of the unmanned aerial vehicle.

[0012] Based on the flight revision parameter of the unmanned aerial vehicle, obtain the corresponding unmanned aerial vehicle control instruction packet according to the preset algorithm on the cloud computer side;

[0013] Send the unmanned aerial vehicle control instruction packet to the corresponding unmanned aerial vehicle control end;

[0014] According to the preset execution order of the unmanned aerial vehicle control instructions, the unmanned aerial vehicle control end executes the unmanned aerial vehicle control instructions corresponding to the unmanned aerial vehicle control instruction packet one by one and feeds back the execution situation to the cloud computer side.

[0015] Mark the time when the unmanned aerial vehicle control end receives the unmanned aerial vehicle control instruction packet;

[0016] According to the time when the unmanned aerial vehicle control end receives the unmanned aerial vehicle control instruction packet twice adjacent, obtain the time interval between the time when the unmanned aerial vehicle control end receives the unmanned aerial vehicle control instruction packet twice adjacent;

[0017] Judge whether the time interval exceeds the first threshold of the preset time interval. If so, the unmanned aerial vehicle control end executes the unmanned aerial vehicle control instruction packet received later; if not, continue to execute the previous unmanned aerial vehicle control instruction packet.

[0018] Obtain the time point when the computer side sends the unmanned aerial vehicle control instruction packet and the time point when it receives the feedback on the execution situation from the unmanned aerial vehicle control end;

[0019] According to the time point when the cloud computer side sends the unmanned aerial vehicle control instruction packet and the time point when it receives the feedback on the execution situation from the unmanned aerial vehicle control end, obtain the time interval between the sending of the unmanned aerial vehicle control instruction and the feedback on the execution situation;

[0020] Judge whether the time interval between the sending of the unmanned aerial vehicle control instruction and the feedback on the execution situation is greater than the second threshold of the preset time interval. If so, trigger a revision blocked warning message and send the flight state data corresponding to the time interval between the sending of the unmanned aerial vehicle control instruction and the feedback on the execution situation to the cloud computer side for storage.

[0021] Obtain the number of flight adjustments of the unmanned aerial vehicle within a preset time period;

[0022] Determine whether the number of flight adjustments of the unmanned aerial vehicle is greater than a preset adjustment number threshold. If so, generate an alarm message and send the alarm message to a preset client for display; if not, the corresponding unmanned aerial vehicle continues to perform flight adjustments.

[0023] The second aspect of the present invention provides an unmanned aerial vehicle status monitoring system for a cloud computer, including a memory and a processor. The memory includes a program for an unmanned aerial vehicle status monitoring method for a cloud computer. When the program for the unmanned aerial vehicle status monitoring method for a cloud computer is executed by the processor, the following steps are implemented:

[0024] Obtain the real-time flight status parameters of the unmanned aerial vehicle;

[0025] Compare and analyze the real-time flight status parameters of the unmanned aerial vehicle with preset ideal flight status parameters to obtain a deviation value of the flight status parameters;

[0026] Determine whether the deviation value of the flight status parameters of the unmanned aerial vehicle exceeds a preset deviation threshold. If so, generate an unmanned aerial vehicle flight revision parameter and send the unmanned aerial vehicle flight revision parameter to the corresponding unmanned aerial vehicle for flight adjustment; if not, display that the unmanned aerial vehicle is flying normally on the cloud computer side.

[0027] Extract the characteristics of the flight status parameters of the unmanned aerial vehicle;

[0028] Query in a preset characteristic resistance coefficient table according to the characteristics of the flight status parameters of the unmanned aerial vehicle to obtain the resistance coefficient of the flight status parameters of the unmanned aerial vehicle corresponding to the characteristics during the current flight;

[0029] Divide the deviation value of the flight status parameters of the unmanned aerial vehicle by the corresponding resistance coefficient to obtain the current unmanned aerial vehicle flight status revision parameter.

[0030] Based on the unmanned aerial vehicle flight revision parameter, obtain a corresponding unmanned aerial vehicle control instruction packet according to a preset algorithm on the cloud computer side;

[0031] Send the unmanned aerial vehicle control instruction packet to the corresponding unmanned aerial vehicle control end;

[0032] According to the preset execution order of the unmanned aerial vehicle control instructions, the unmanned aerial vehicle control end sequentially executes the unmanned aerial vehicle control instructions corresponding to the unmanned aerial vehicle control instruction packet and feeds back the execution situation to the cloud computer side.

[0033] Mark the time when the unmanned aerial vehicle control terminal receives the unmanned aerial vehicle control instruction packet;

[0034] According to the time when the unmanned aerial vehicle control terminal receives the unmanned aerial vehicle control instruction packet twice in succession, obtain the interval duration between the times when the unmanned aerial vehicle control terminal receives the unmanned aerial vehicle control instruction packet twice in succession;

[0035] Judge whether the interval duration exceeds a first threshold value of the preset interval duration. If so, the unmanned aerial vehicle control terminal executes the subsequently received unmanned aerial vehicle control instruction packet; if not, continue to execute the previous unmanned aerial vehicle control instruction packet.

[0036] Obtain the time point when the computer terminal sends the unmanned aerial vehicle control instruction packet and the time point when it receives the feedback on the execution status from the unmanned aerial vehicle control terminal;

[0037] According to the time point when the cloud computer terminal sends the unmanned aerial vehicle control instruction packet and the time point when it receives the feedback on the execution status from the unmanned aerial vehicle control terminal, obtain the time interval between the sending of the unmanned aerial vehicle control instruction and the feedback on the execution status;

[0038] Judge whether the time interval between the sending of the unmanned aerial vehicle control instruction and the feedback on the execution status is greater than a second threshold value of the preset interval duration. If so, trigger a warning message for revision obstruction, and send the flight status data corresponding to the time interval between the sending of the unmanned aerial vehicle control instruction and the feedback on the execution status to the cloud computer terminal for storage.

[0039] In this solution, it further includes:

[0040] Obtain the number of flight adjustments of the unmanned aerial vehicle within a preset time period;

[0041] Judge whether the number of flight adjustments of the unmanned aerial vehicle is greater than a preset adjustment number threshold value. If so, generate an alarm message and send the alarm message to a preset client for display; if not, the corresponding unmanned aerial vehicle continues to execute the flight adjustment.

[0042] The third aspect of the present invention provides a computer-readable storage medium, in which a program for the method of monitoring the state of an unmanned aerial vehicle for a cloud computer is stored. When the program for the method of monitoring the state of an unmanned aerial vehicle for a cloud computer is executed by a processor, the steps of the method of monitoring the state of an unmanned aerial vehicle for a cloud computer as described in any one of the above are implemented.

[0043] A method, system, and medium for monitoring the state of an unmanned aerial vehicle for a cloud computer according to the present invention can, through connection with a cloud computer system, monitor the flight state of the unmanned aerial vehicle in real time and provide accurate flight parameter data. By utilizing the computing and storage capabilities of the cloud computer, a large amount of flight parameter data can be efficiently processed and analyzed. The cloud computer system can store the flight parameter data of the unmanned aerial vehicle to form historical data records, which can be used for subsequent retrospective analysis, troubleshooting, and flight mode optimization. By utilizing the historical data, the safety, reliability, and efficiency of the flight of the unmanned aerial vehicle can be further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The flowchart of a method for monitoring the state of an unmanned aerial vehicle for a cloud computer according to the present invention is shown;

[0045] Figure 2 The block diagram of a system for monitoring the state of an unmanned aerial vehicle for a cloud computer according to the present invention is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] In order to more clearly understand the above objects, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0047] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0048] Figure 1 The flowchart of a method for monitoring the state of an unmanned aerial vehicle for a cloud computer according to the present invention is shown;

[0049] As Figure 1 shown, the present invention discloses a method for monitoring the state of an unmanned aerial vehicle for a cloud computer, including:

[0050] S101, obtaining real-time flight state parameters of the unmanned aerial vehicle;

[0051] S102, comparing and analyzing the real-time flight state parameters of the unmanned aerial vehicle with preset ideal flight state parameters to obtain a deviation value of the flight state parameters;

[0052] S103. Determine whether the deviation value of the flight state parameters of the unmanned aerial vehicle exceeds a preset deviation threshold. If so, generate flight revision parameters for the unmanned aerial vehicle and send the flight revision parameters of the unmanned aerial vehicle to the corresponding unmanned aerial vehicle for flight adjustment; if not, display that the flight of the unmanned aerial vehicle is normal on the cloud computer terminal.

[0053] It should be noted that during the process of the unmanned aerial vehicle performing a flight mission, it can obtain the real-time flight state parameters of the unmanned aerial vehicle by using sensors and a flight control system. These parameters can include relevant information such as the attitude angle, position coordinates, speed, altitude, battery power, etc. of the unmanned aerial vehicle. The sensors and the flight control system will collect these parameters in real time and transmit them to the cloud computer system for processing. The real-time flight state parameters of the unmanned aerial vehicle are compared and analyzed with the preset ideal flight state parameters. By comparing the difference between the real-time parameters and the ideal parameters, the deviation value of the flight state parameters can be calculated. These deviation values can represent the gap between the current flight state of the unmanned aerial vehicle and the expected flight state. The calculated deviation value of the flight state parameters is compared with the preset deviation threshold. If the deviation value exceeds the preset threshold, it indicates that the flight state of the unmanned aerial vehicle is abnormal or deviates from the expected flight state. At this time, proceed to the next step. If the deviation value does not exceed the preset threshold, it indicates that the flight state of the unmanned aerial vehicle is normal, and the information that the flight of the unmanned aerial vehicle is normal can be displayed on the cloud computer terminal; when the deviation value of the flight state parameters of the unmanned aerial vehicle exceeds the preset deviation threshold, generate flight revision parameters for the unmanned aerial vehicle. The revision parameters can include attitude adjustment, heading adjustment, altitude adjustment, etc. according to specific situations. The generated revision parameters will be sent to the corresponding unmanned aerial vehicle for flight adjustment to correct the flight state deviation and make it return to the ideal flight state.

[0054] According to an embodiment of the present invention, it further includes:

[0055] Extract the characteristics of the flight state parameters of the unmanned aerial vehicle;

[0056] Query in a preset characteristic drag coefficient table according to the characteristics of the flight state parameters of the unmanned aerial vehicle to obtain the drag coefficient of the flight state parameters of the unmanned aerial vehicle corresponding to the characteristics during the current flight;

[0057] Divide the deviation value of the flight state parameters of the unmanned aerial vehicle by the corresponding drag coefficient to obtain the current flight revision parameters of the unmanned aerial vehicle.

[0058] It should be noted that representative features are extracted from the obtained flight state parameters of the unmanned aerial vehicle. These features can be selected according to specific requirements, such as the amplitude of the attitude angle, the change speed of the position coordinates, the change rate of the altitude, etc. By extracting the features, the complex flight state parameters can be simplified into more representative and comparable characteristic values. The extracted features of the unmanned aerial vehicle flight state parameters are matched and queried with the preset characteristic drag coefficient table. The preset characteristic drag coefficient table lists the drag coefficients corresponding to various features. According to the value of the feature, the drag coefficient corresponding to the feature is found in the table. This drag coefficient reflects the influence degree of the feature on the unmanned aerial vehicle flight state parameters. In this way, according to the influence degree of the feature, the deviation value of the flight state parameters can be adjusted, and the obtained result is the revised parameter of the current unmanned aerial vehicle flight state. It takes into account the influence of the feature on the flight state, making the revised parameter more accurate and adaptable to the current flight state. The embodiment of the present invention further extracts the features of the flight state parameters on the basis of the original unmanned aerial vehicle state monitoring method, and queries in combination with the characteristic drag coefficient table to obtain a more accurate revised parameter of the unmanned aerial vehicle flight state. Such a revised parameter takes into account the influence of the feature on the flight state and can more precisely guide the flight adjustment of the unmanned aerial vehicle.

[0059] According to an embodiment of the present invention, it further includes:

[0060] Based on the revised parameter of the unmanned aerial vehicle flight, a corresponding unmanned aerial vehicle control instruction packet is obtained according to the preset algorithm on the cloud computer side;

[0061] The unmanned aerial vehicle control instruction packet is sent to the corresponding unmanned aerial vehicle control end;

[0062] According to the preset execution order of the unmanned aerial vehicle control instructions, the unmanned aerial vehicle control end sequentially executes the unmanned aerial vehicle control instructions corresponding to the unmanned aerial vehicle control instruction packet and feeds back the execution situation to the cloud computer side.

[0063] It should be noted that in this step, based on the calculated flight revision parameters of the unmanned aerial vehicle, corresponding unmanned aerial vehicle control instruction packets are generated on the cloud computer side using a preset algorithm. These instruction packets contain a series of control instructions for adjusting flight parameters such as the attitude, heading, and altitude of the unmanned aerial vehicle to correct the flight state. The generated unmanned aerial vehicle control instruction packets are sent to the corresponding unmanned aerial vehicle control terminal through wireless communication or other appropriate means. The unmanned aerial vehicle control terminal can be the flight control system on the unmanned aerial vehicle responsible for receiving and executing control instructions from the cloud computer side. These instructions can include adjusting the attitude, changing the heading, and altering the altitude, etc., to correct and control the flight state of the unmanned aerial vehicle. During the execution process, the unmanned aerial vehicle control terminal will feedback the execution status to the cloud computer side in real time for monitoring and recording the flight state correction process of the unmanned aerial vehicle.

[0064] According to an embodiment of the present invention, it further includes:

[0065] Mark the time when the unmanned aerial vehicle control terminal receives the unmanned aerial vehicle control instruction packet;

[0066] Based on the time when the unmanned aerial vehicle control terminal receives the unmanned aerial vehicle control instruction packet twice in succession, obtain the interval duration between the two times when the unmanned aerial vehicle control terminal receives the unmanned aerial vehicle control instruction packet;

[0067] Judge whether the interval duration exceeds a first threshold of the preset interval duration. If so, the unmanned aerial vehicle control terminal executes the unmanned aerial vehicle control instruction packet received later; if not, continue to execute the previous unmanned aerial vehicle control instruction packet.

[0068] It should be noted that this embodiment provides an instruction judgment mechanism, specifically: mark the reception time of the unmanned aerial vehicle control instruction packet; based on the reception time of adjacent instruction packets, obtain the interval duration; judge whether the interval duration exceeds a first threshold of the preset interval duration. If so, the unmanned aerial vehicle control terminal executes the unmanned aerial vehicle control instruction packet received later; if not, continue to execute the previous unmanned aerial vehicle control instruction packet.

[0069] In this embodiment, when the unmanned aerial vehicle control terminal receives each unmanned aerial vehicle control instruction packet, it records the time when the instruction packet is received. This time mark can obtain this interval duration by calculating the time difference or timestamp, and determines whether the received interval duration between two adjacent unmanned aerial vehicle control instruction packets exceeds the first threshold of the preset interval duration. If the interval duration exceeds the threshold, it indicates that the time interval between the control instruction packets is too long, and there may be communication delays or other problems. At this time, the unmanned aerial vehicle control terminal should execute the later received unmanned aerial vehicle control instruction packet. If the interval duration does not exceed the threshold, it continues to execute the previous unmanned aerial vehicle control instruction packet to maintain the continuity of flight control. In the embodiment of the present invention, during the execution of the original unmanned aerial vehicle control instruction, the marking of the instruction packet reception time and the judgment of the interval duration are added. By judging whether the time interval of receiving the instruction packet exceeds the preset threshold, possible communication delays or other abnormal situations can be detected and processed to ensure the timeliness and stability of the unmanned aerial vehicle control.

[0070] According to the embodiment of the present invention, it further includes:

[0071] Obtain the time point when the computer terminal sends the unmanned aerial vehicle control instruction packet and the time point when it receives the feedback on the execution situation from the unmanned aerial vehicle control terminal;

[0072] According to the time point when the cloud computer terminal sends the unmanned aerial vehicle control instruction packet and the time point when it receives the feedback on the execution situation from the unmanned aerial vehicle control terminal, obtain the time interval between the sending of the unmanned aerial vehicle control instruction and the feedback on the execution situation;

[0073] Judge whether the time interval between the sending of the unmanned aerial vehicle control instruction and the feedback on the execution situation is greater than the second threshold of the preset interval duration. If so, trigger a warning message for the blocked revision, and send the flight state data corresponding to the time interval between the sending of the unmanned aerial vehicle control instruction and the feedback on the execution situation to the cloud computer terminal for storage.

[0074] It should be noted that this embodiment provides an instruction execution time warning mechanism, which specifically includes: based on the sending time point and the feedback time point of the control instruction packet, obtain the execution time interval; judge whether the execution time interval is greater than the preset second threshold; if so, trigger a warning message for the blocked revision, and send the sending time point and the feedback time point of the control instruction packet to the background for storage.

[0075] In this embodiment, the time points when the computer terminal sends the unmanned aerial vehicle control instruction packet and when the unmanned aerial vehicle control terminal receives the execution status and gives feedback are obtained. These time points can be used to calculate the time interval between the sending of the unmanned aerial vehicle control instruction and the feedback of the execution status. This time interval can be used to evaluate the transmission delay or execution delay of the unmanned aerial vehicle control instruction. It is judged whether the calculated time interval between the sending of the unmanned aerial vehicle control instruction and the feedback of the execution status exceeds the second threshold of the preset interval duration. If the time interval exceeds the threshold, it indicates that there is an abnormality in the transmission or execution of the control instruction. At this time, a warning message for revision obstruction is triggered. At the same time, the flight status data corresponding to this time interval is sent to the cloud computer terminal for storage for subsequent analysis and recording. When the cloud computer terminal receives the warning message for revision obstruction, a preset emergency procedure can be triggered. The preset emergency procedure retrieves the unmanned aerial vehicle flight status data corresponding to the time interval between the sending of the unmanned aerial vehicle control instruction and the feedback of the situation and obtains the real-time environment information and the current task information. Analyze the current flight status of the unmanned aerial vehicle according to the unmanned aerial vehicle flight status data, real-time environment information and current task information, and re-trigger the flight adjustment of the unmanned aerial vehicle.

[0076] According to an embodiment of the present invention, it further includes:

[0077] Obtain the number of flight adjustments of the unmanned aerial vehicle within a preset time period;

[0078] Judge whether the number of flight adjustments of the unmanned aerial vehicle is greater than the preset adjustment number threshold. If so, generate an alarm message and send the alarm message to a preset client for display; if not, the corresponding unmanned aerial vehicle continues to perform flight adjustments.

[0079] It should be noted that the number of flight adjustments made by the unmanned aerial vehicle within a preset time period is counted. These adjustments can include attitude adjustment, heading adjustment, altitude adjustment, etc. It is judged whether the number of flight adjustments of the unmanned aerial vehicle within the preset time period exceeds the preset adjustment number threshold. If the number of adjustments exceeds the threshold, it means that the flight adjustment frequency of the unmanned aerial vehicle within the preset time period is too high. For example, if the preset adjustment number threshold is 5 times, when the number of flight adjustments of the unmanned aerial vehicle within the preset time period exceeds 5 times, it indicates that the flight status of the unmanned aerial vehicle needs attention or correction, and it can also reflect a sudden change in the external environment. The ideal flight status parameters used as a comparison reference are no longer suitable for the current real-time environment. At this time, an alarm message is generated, and the generated alarm message is sent to a preset client for display so that the operator can obtain and analyze it in time.

[0080] It is worth mentioning that it further includes:

[0081] Obtain the unmanned aerial vehicle number information;

[0082] Generate a corresponding communication private key for the unmanned aerial vehicle according to the unmanned aerial vehicle number information;

[0083] Send the communication private key of the unmanned aerial vehicle to the corresponding unmanned aerial vehicle for storage;

[0084] Generate a corresponding communication lock on the cloud computer side according to the communication private key of the unmanned aerial vehicle;

[0085] Encrypt the communication data sent by the cloud computer based on the communication lock corresponding to the unmanned aerial vehicle;

[0086] Judge whether the communication private key of the unmanned aerial vehicle matches the communication lock for receiving the encrypted communication data. If so, allow the communication data to pass and be read; if not, do not allow the communication data to pass.

[0087] It should be noted that this embodiment provides a transmission process of the communication private key, which specifically includes: generating a communication private key based on the unmanned aerial vehicle number information and sending it to the corresponding unmanned aerial vehicle for storage; generating a communication lock based on the communication private key of the machine for encrypting the communication data sent by the cloud computer; the unmanned aerial vehicle judges whether it can decrypt the communication data based on the communication private key. If so, allow the communication data to pass and be read; if not, do not allow the communication data to pass.

[0088] In this embodiment, the unique identification information associated with the unmanned aerial vehicle is obtained. This identification number can be used to identify a specific unmanned aerial vehicle device, and an unmanned aerial vehicle communication private key corresponding to this number is generated. The communication private key is the key used to encrypt and decrypt the communication data between the unmanned aerial vehicle and the cloud computer. The generated unmanned aerial vehicle communication private key is sent to the corresponding unmanned aerial vehicle device for storage. The unmanned aerial vehicle needs to save the communication private key for use in subsequent communication data encryption and decryption processes. On the cloud computer side, according to the unmanned aerial vehicle communication private key, a corresponding communication secret lock is generated. The communication secret lock is the key used to encrypt and decrypt the communication data between the cloud computer and the unmanned aerial vehicle. The communication data sent by the cloud computer is encrypted using the communication secret lock corresponding to the unmanned aerial vehicle, which can ensure the security and confidentiality of the communication data during transmission. It is determined whether the communication private key of the unmanned aerial vehicle matches the communication secret lock used for the received encrypted communication data. If they match, it indicates that the communication data is legal and the communication data can be read through decryption. If they do not match, it means that the sender of the communication data may be illegal or tampered with, and at this time, the communication data is not allowed to pass. By generating the unmanned aerial vehicle communication private key and the corresponding communication secret lock, the encryption and decryption of the communication data are realized. At the same time, by judging the matching degree between the communication private key and the communication secret lock, the security and legality of the communication data are ensured, which helps to protect the communication content between the unmanned aerial vehicle and the cloud computer and prevent unauthorized access and data leakage.

[0089] It is worth mentioning that it also includes:

[0090] Obtain the current environmental information;

[0091] Obtain the historical mission data of the unmanned aerial vehicle and the performance parameters of the unmanned aerial vehicle;

[0092] Send the historical mission data of the unmanned aerial vehicle, the performance parameters of the unmanned aerial vehicle, the current unmanned aerial vehicle mission information, and the environmental information of the mission location to the cloud computer side;

[0093] The cloud computer side receives the historical mission data of the unmanned aerial vehicle, the performance parameters of the unmanned aerial vehicle, and the environmental information of the mission location, and constructs an ideal flight state model of the unmanned aerial vehicle according to the preset first mathematical model;

[0094] Send the current unmanned aerial vehicle mission information to the ideal flight state model of the unmanned aerial vehicle to obtain the predicted value of the ideal flight state parameters of the current unmanned aerial vehicle when performing the mission;

[0095] Set the predicted value of the ideal flight state parameters of the unmanned aerial vehicle when performing the mission as the preset ideal flight state parameters of the unmanned aerial vehicle.

[0096] It should be noted that this embodiment provides a flight state parameter prediction process, which specifically includes: inputting the historical mission data, performance parameters, current mission information, and environmental information of the task location of the unmanned aerial vehicle into a preset first mathematical model to construct an ideal flight state model of the unmanned aerial vehicle;

[0097] Based on the ideal flight state model, obtain the predicted values of the ideal flight state parameters for setting the preset ideal flight state parameters of the unmanned aerial vehicle.

[0098] In this embodiment, relevant information about the environment where the current unmanned aerial vehicle is located is obtained, such as meteorological data, terrain information, obstacle positions, etc. These environmental information can be used to evaluate the safety and adaptability of the unmanned aerial vehicle's flight. The task data and relevant performance parameters completed by the unmanned aerial vehicle in the past are obtained. The historical mission data can include information such as flight trajectories and mission execution times, and the performance parameters can cover indicators such as flight speed, endurance, and load capacity. The obtained historical mission data, performance parameters, current mission information, and environmental information of the task location of the unmanned aerial vehicle are sent to the cloud computer terminal. These data will be used for subsequent analysis, modeling, and optimization processes. On the cloud computer terminal, after receiving the historical mission data, performance parameters, and environmental information of the unmanned aerial vehicle, according to the preset first mathematical model, such as a neural network model, use these data to construct an ideal flight state model of the unmanned aerial vehicle. Through mathematical modeling and analysis, and combined with the current mission information of the unmanned aerial vehicle, obtain the predicted values of the ideal flight state parameters of the corresponding unmanned aerial vehicle, including attitude, speed, altitude, etc., and set the predicted values of the ideal flight state parameters of the unmanned aerial vehicle as the preset ideal flight state parameters of the unmanned aerial vehicle.

[0099] It is worth mentioning that it also includes:

[0100] Collect the historical flight parameters, environmental data, and adjustment records of the unmanned aerial vehicle, train the flight parameter optimization model of the unmanned aerial vehicle, and obtain the dynamic parameter generation strategy;

[0101] Based on the dynamic parameter generation strategy, according to the flight parameters of the unmanned aerial vehicle, output the predictive flight revision parameters for generating a pre-adjustment instruction package;

[0102] The control end of the unmanned aerial vehicle performs flight pre-adjustment according to the pre-adjustment instruction package, and after execution, feeds back the deviation between the actual flight state and the predicted value to the cloud for online iterative optimization of the reinforcement learning model.

[0103] It should be noted that flight parameters, environmental data, and past adjustment records are extracted from historical data stored in the cloud to construct a labeled training set. Among them, flight parameters include but are not limited to flight attitude, flight speed, and flight direction. Environmental data includes but is not limited to wind speed, temperature, and humidity. Past adjustment records include but are not limited to correction parameters and correction effect scores. Based on the training data set, an unmanned aerial vehicle flight parameter optimization model is trained to enable the model to learn how to generate optimal revised parameters in a specific environment, realize the model's autonomous exploration of the optimal adjustment strategy, and reduce the dependence on manual rules. The current flight state and environmental data of the unmanned aerial vehicle are input into the model in real time, and the model outputs the predicted revised parameters for the future time window. The cloud converts the predicted parameters into an instruction packet and sends it to the corresponding unmanned aerial vehicle. After the unmanned aerial vehicle performs pre-adjustment, it feeds back the actual flight state to the cloud for evaluating the prediction accuracy. The cloud performs online fine-tuning on the model according to the feedback data and updates the strategy to adapt to environmental changes. By using reinforcement learning to predict the flight state trend and intervening in the adjustment in advance, the lag problem of traditional reactive control is solved; and the model is iteratively optimized online to adapt to complex environmental changes.

[0104] Figure 2 FIG. shows a block diagram of an unmanned aerial vehicle state monitoring system for a cloud computer according to the present invention.

[0105] As Figure 2 shown, a second aspect of the present invention provides an unmanned aerial vehicle state monitoring system 2 for a cloud computer, including a memory 21 and a processor 22. The memory includes an unmanned aerial vehicle state monitoring method program for a cloud computer. When the unmanned aerial vehicle state monitoring method program for a cloud computer is executed by the processor, the following steps are implemented:

[0106] Obtain the real-time flight state parameters of the unmanned aerial vehicle;

[0107] Compare and analyze the real-time flight state parameters of the unmanned aerial vehicle with the preset ideal flight state parameters to obtain the deviation value of the flight state parameters;

[0108] Judge whether the deviation value of the unmanned aerial vehicle flight state parameters exceeds the preset deviation threshold. If so, generate the unmanned aerial vehicle flight revised parameters and send the unmanned aerial vehicle flight revised parameters to the corresponding unmanned aerial vehicle for flight adjustment; if not, display that the unmanned aerial vehicle is flying normally on the cloud computer side.

[0109] It should be noted that during the execution of flight missions, an unmanned aerial vehicle can obtain real-time flight status parameters of the unmanned aerial vehicle by using sensors and a flight control system. These parameters can include relevant information such as the attitude angle, position coordinates, speed, altitude, battery power, etc. of the unmanned aerial vehicle. The sensors and the flight control system will collect these parameters in real time and transmit them to the cloud computer system for processing. The real-time flight status parameters of the unmanned aerial vehicle are compared and analyzed with the preset ideal flight status parameters. By comparing the differences between the real-time parameters and the ideal parameters, the deviation values of the flight status parameters can be calculated. These deviation values can represent the gap between the current flight status of the unmanned aerial vehicle and the expected flight status. The calculated deviation values of the flight status parameters are compared with the preset deviation threshold. If the deviation value exceeds the preset threshold, it indicates that the flight status of the unmanned aerial vehicle is abnormal or deviates from the expected flight status. At this time, the next step is entered. If the deviation value does not exceed the preset threshold, it indicates that the flight status of the unmanned aerial vehicle is normal, and the information that the unmanned aerial vehicle is flying normally can be displayed on the cloud computer terminal; when the deviation value of the flight status parameters of the unmanned aerial vehicle exceeds the preset deviation threshold, revised parameters for the flight of the unmanned aerial vehicle are generated. The revised parameters can include attitude adjustment, heading adjustment, altitude adjustment, etc. according to specific situations. The generated revised parameters will be sent to the corresponding unmanned aerial vehicle for flight adjustment to correct the flight status deviation and make it return to the ideal flight status.

[0110] According to an embodiment of the present invention, it further includes:

[0111] Extract the characteristics of the flight status parameters of the unmanned aerial vehicle;

[0112] Query in the preset characteristic drag coefficient table according to the characteristics of the flight status parameters of the unmanned aerial vehicle to obtain the drag coefficient of the flight status parameters of the unmanned aerial vehicle corresponding to the characteristics during the current flight;

[0113] Divide the deviation value of the flight status parameters of the unmanned aerial vehicle by the corresponding drag coefficient to obtain the current revised parameters for the flight status of the unmanned aerial vehicle.

[0114] It should be noted that representative features are extracted from the obtained flight state parameters of the unmanned aerial vehicle. These features can be selected according to specific requirements, such as the amplitude of the attitude angle, the change speed of the position coordinates, the change rate of the altitude, etc. By extracting the features, the complex flight state parameters can be simplified into more representative and comparable feature values. The extracted features of the unmanned aerial vehicle flight state parameters are matched and queried with a preset feature drag coefficient table. The preset feature drag coefficient table lists the drag coefficients corresponding to various features. According to the value of the feature, the drag coefficient corresponding to the feature is found in the table. This drag coefficient reflects the influence degree of the feature on the unmanned aerial vehicle flight state parameters. In this way, according to the influence degree of the feature, the deviation value of the flight state parameters can be adjusted, and the obtained result is the revised parameter of the current unmanned aerial vehicle flight state. It takes into account the influence of the feature on the flight state, making the revised parameter more accurate and adaptable to the current flight state. The embodiment of the present invention further extracts the features of the flight state parameters on the basis of the original unmanned aerial vehicle state monitoring method, and combines with the feature drag coefficient table for query to obtain a more accurate revised parameter of the unmanned aerial vehicle flight state. Such a revised parameter takes into account the influence of the feature on the flight state and can more precisely guide the flight adjustment of the unmanned aerial vehicle.

[0115] According to an embodiment of the present invention, it further includes:

[0116] Based on the revised parameter of the unmanned aerial vehicle flight, a corresponding unmanned aerial vehicle control instruction packet is obtained according to a preset algorithm on the cloud computer side;

[0117] The unmanned aerial vehicle control instruction packet is sent to the corresponding unmanned aerial vehicle control end;

[0118] According to the preset execution order of the unmanned aerial vehicle control instructions, the unmanned aerial vehicle control end executes one by one the unmanned aerial vehicle control instructions corresponding to the unmanned aerial vehicle control instruction packet and feeds back the execution situation to the cloud computer side.

[0119] It should be noted that in this step, based on the calculated flight revision parameters of the unmanned aerial vehicle, corresponding unmanned aerial vehicle control instruction packets are generated on the cloud computer side using a preset algorithm. These instruction packets contain a series of control instructions for adjusting flight parameters such as the attitude, heading, and altitude of the unmanned aerial vehicle to correct the flight state. The generated unmanned aerial vehicle control instruction packets are sent to the corresponding unmanned aerial vehicle control terminal through wireless communication or other appropriate means. The unmanned aerial vehicle control terminal can be the flight control system on the unmanned aerial vehicle, which is responsible for receiving and executing the control instructions from the cloud computer side. These instructions can include adjusting the attitude, changing the heading, and altering the altitude, etc., to correct and control the flight state of the unmanned aerial vehicle. During the execution process, the unmanned aerial vehicle control terminal will feedback the execution status to the cloud computer side in real time for monitoring and recording the flight state correction process of the unmanned aerial vehicle.

[0120] According to an embodiment of the present invention, it further includes:

[0121] Mark the time when the unmanned aerial vehicle control terminal receives the unmanned aerial vehicle control instruction packet;

[0122] Based on the time when the unmanned aerial vehicle control terminal receives the unmanned aerial vehicle control instruction packet twice in succession, obtain the time interval between the two times when the unmanned aerial vehicle control terminal receives the unmanned aerial vehicle control instruction packet;

[0123] Judge whether the time interval exceeds the first threshold of the preset time interval. If so, the unmanned aerial vehicle control terminal executes the unmanned aerial vehicle control instruction packet received later; if not, continue to execute the previous unmanned aerial vehicle control instruction packet.

[0124] It should be noted that this embodiment provides an instruction judgment mechanism, specifically: mark the reception time of the unmanned aerial vehicle control instruction packet; based on the reception time of adjacent instruction packets, obtain the time interval; judge whether the time interval exceeds the first threshold of the preset time interval. If so, the unmanned aerial vehicle control terminal executes the unmanned aerial vehicle control instruction packet received later; if not, continue to execute the previous unmanned aerial vehicle control instruction packet.

[0125] In this embodiment, when the unmanned aerial vehicle (UAV) control terminal receives each UAV control instruction packet, it records the time when the packet is received. This time mark can be used to obtain the interval duration by calculating the time difference or timestamp. It is then determined whether the interval duration between two adjacent received UAV control instruction packets exceeds a preset first threshold for the interval duration. If the interval duration exceeds the threshold, it indicates that the time interval between control instruction packets is too long, and there may be communication delays or other problems. In this case, the UAV control terminal should execute the later-received UAV control instruction packet. If the interval duration does not exceed the threshold, the previous UAV control instruction packet is continued to be executed to maintain the continuity of flight control. In the embodiment of the present invention, during the execution of the original UAV control instruction, the marking of the receiving time of the instruction packet and the judgment of the interval duration are added. By determining whether the time interval for receiving instruction packets exceeds a preset threshold, possible communication delays or other abnormal situations can be detected and processed, ensuring the timeliness and stability of UAV control.

[0126] According to an embodiment of the present invention, it further includes:

[0127] Obtain the time point when the computer terminal sends the UAV control instruction packet and the time point when the UAV control terminal receives the feedback on the execution status.

[0128] Based on the time point when the cloud computer terminal sends the UAV control instruction packet and the time point when it receives the feedback on the execution status from the UAV control terminal, obtain the time interval between the sending of the UAV control instruction and the feedback on the execution status.

[0129] Determine whether the time interval between the sending of the UAV control instruction and the feedback on the execution status is greater than a preset second threshold for the interval duration. If so, trigger a warning message for a blocked revision, and send the flight status data corresponding to the time interval between the sending of the UAV control instruction and the feedback on the execution status to the cloud computer terminal for storage.

[0130] It should be noted that this embodiment provides an instruction execution time warning mechanism, which specifically includes: based on the sending time point and the feedback time point of the control instruction packet, obtain the execution time interval; determine whether the execution time interval is greater than a preset second threshold; if so, trigger a warning message for a blocked revision, and send the sending time point and the feedback time point of the control instruction packet to the background for storage.

[0131] In this embodiment, obtain the time point when the computer terminal sends the unmanned aerial vehicle control instruction packet and the time point when the unmanned aerial vehicle control terminal receives the execution situation and gives feedback. These time points can be used to calculate the time interval between the sending of the unmanned aerial vehicle control instruction and the feedback of the execution situation. This time interval can be used to evaluate the transmission delay or execution delay of the unmanned aerial vehicle control instruction. Determine whether the calculated time interval between the sending of the unmanned aerial vehicle control instruction and the feedback of the execution situation exceeds the second threshold of the preset interval duration. If the time interval exceeds the threshold, it indicates that there is an abnormality in the transmission or execution of the control instruction. At this time, trigger a warning message for revision obstruction; at the same time, send the flight state data corresponding to this time interval to the cloud computer terminal for storage for subsequent analysis and recording. When the cloud computer terminal receives the warning message for revision obstruction, it can trigger a preset emergency procedure. The preset emergency procedure retrieves the unmanned aerial vehicle flight state data corresponding to the time interval between the sending of the unmanned aerial vehicle control instruction and the situation feedback, and obtains the real-time environment information and the current task information; analyze the current flight state of the unmanned aerial vehicle according to the unmanned aerial vehicle flight state data, the real-time environment information and the current task information, and re-trigger the flight adjustment of the unmanned aerial vehicle.

[0132] According to an embodiment of the present invention, it further includes:

[0133] Obtain the number of flight adjustments of the unmanned aerial vehicle within a preset time period;

[0134] Judge whether the number of flight adjustments of the unmanned aerial vehicle is greater than a preset adjustment number threshold. If so, generate an alarm message and send the alarm message to a preset client for display; if not, the corresponding unmanned aerial vehicle continues to perform flight adjustments.

[0135] It should be noted that count the number of flight adjustments made by the unmanned aerial vehicle within a preset time period. These adjustments can include attitude adjustment, heading adjustment, altitude adjustment, etc. Judge whether the number of flight adjustments of the unmanned aerial vehicle within the preset time period exceeds the preset adjustment number threshold. If the number of adjustments exceeds the threshold, it means that the flight adjustment frequency of the unmanned aerial vehicle within the preset time period is too high. For example, if the preset adjustment number threshold is 5 times, then when the number of flight adjustments of the unmanned aerial vehicle within the preset time period exceeds 5 times, it indicates that the flight state of the unmanned aerial vehicle needs attention or correction, and it can also reflect a sudden change in the external environment. The ideal flight state parameters used as a comparison reference are no longer suitable for the current real-time environment. At this time, generate an alarm message, and the generated alarm message will be sent to a preset client for display so that the operator can obtain and analyze and process it in time.

[0136] It is worth mentioning that it further includes:

[0137] Obtain the unmanned aerial vehicle number information;

[0138] Generate a corresponding communication private key for the unmanned aerial vehicle according to the unmanned aerial vehicle number information;

[0139] Send the communication private key of the unmanned aerial vehicle to the corresponding unmanned aerial vehicle for storage;

[0140] Generate a corresponding communication lock on the cloud computer side according to the communication private key of the unmanned aerial vehicle;

[0141] Encrypt the communication data sent by the cloud computer based on the communication lock corresponding to the unmanned aerial vehicle;

[0142] Determine whether the communication private key of the unmanned aerial vehicle matches the communication lock for receiving the encrypted communication data. If so, allow the communication data to pass and be read; if not, do not allow the communication data to pass.

[0143] It should be noted that this embodiment provides a transmission process of a communication private key, which specifically includes: generating a communication private key based on the unmanned aerial vehicle number information and sending it to the corresponding unmanned aerial vehicle for storage; generating a communication lock based on the communication private key of the machine for encrypting the communication data sent by the cloud computer; the unmanned aerial vehicle determines whether it can decrypt the communication data based on the communication private key. If so, allow the communication data to pass and be read; if not, do not allow the communication data to pass.

[0144] In this embodiment, the unique identification information associated with the unmanned aerial vehicle is obtained. This identification number can be used to identify a specific unmanned aerial vehicle device, and the communication private key corresponding to this number is generated. The communication private key is the key used to encrypt and decrypt the communication data between the unmanned aerial vehicle and the cloud computer. The generated communication private key of the unmanned aerial vehicle is sent to the corresponding unmanned aerial vehicle device for storage. The unmanned aerial vehicle needs to save the communication private key for subsequent use in the communication data encryption and decryption process. On the cloud computer side, according to the communication private key of the unmanned aerial vehicle, the corresponding communication secret lock is generated. The communication secret lock is the key used to encrypt and decrypt the communication data between the cloud computer and the unmanned aerial vehicle. The communication data sent by the cloud computer is encrypted using the communication secret lock corresponding to the unmanned aerial vehicle, which can ensure the security and confidentiality of the communication data during the transmission process. It is determined whether the communication private key of the unmanned aerial vehicle matches the communication secret lock used for the received encrypted communication data. If they match, it indicates that the communication data is legal and the communication data can be read by decryption. If they do not match, it means that the sender of the communication data may be illegal or tampered with, and at this time, the communication data is not allowed to pass. By generating the communication private key of the unmanned aerial vehicle and the corresponding communication secret lock, the encryption and decryption of the communication data are realized. At the same time, by judging the matching degree between the communication private key and the communication secret lock, the security and legality of the communication data are ensured, which helps to protect the communication content between the unmanned aerial vehicle and the cloud computer and prevent unauthorized access and data leakage.

[0145] It is worth mentioning that it further includes:

[0146] Obtain the current environmental information;

[0147] Obtain the historical task data of the unmanned aerial vehicle and the performance parameters of the unmanned aerial vehicle;

[0148] Send the historical task data of the unmanned aerial vehicle, the performance parameters of the unmanned aerial vehicle, the current unmanned aerial vehicle task information, and the environmental information of the task location to the cloud computer side;

[0149] The cloud computer side receives the historical task data of the unmanned aerial vehicle, the performance parameters of the unmanned aerial vehicle, and the environmental information of the task location, and constructs an ideal flight state model of the unmanned aerial vehicle according to the preset first mathematical model;

[0150] Send the current unmanned aerial vehicle task information to the ideal flight state model of the unmanned aerial vehicle to obtain the predicted value of the ideal flight state parameters when the current unmanned aerial vehicle is performing the task;

[0151] Set the predicted value of the ideal flight state parameters when the unmanned aerial vehicle is performing the task as the preset ideal flight state parameters of the unmanned aerial vehicle.

[0152] It should be noted that this embodiment provides a flight state parameter prediction process, which specifically includes: inputting the historical mission data, performance parameters, current mission information, and environmental information of the task location of the unmanned aerial vehicle into a preset first mathematical model to construct an ideal flight state model of the unmanned aerial vehicle;

[0153] Based on the ideal flight state model, obtain the predicted values of the ideal flight state parameters, which are used to set the preset ideal flight state parameters of the unmanned aerial vehicle.

[0154] In this embodiment, relevant information about the environment where the current unmanned aerial vehicle is located is obtained, such as meteorological data, terrain information, obstacle positions, etc. These environmental information can be used to evaluate the safety and adaptability of the unmanned aerial vehicle's flight. The task data and related performance parameters completed by the unmanned aerial vehicle in the past are obtained. The historical mission data can include information such as flight trajectories and mission execution times, and the performance parameters can cover indicators such as flight speed, endurance, and load capacity. The obtained historical mission data, performance parameters, current mission information, and environmental information of the task location of the unmanned aerial vehicle are sent to the cloud computer terminal. These data will be used for subsequent analysis, modeling, and optimization processes. On the cloud computer terminal, after receiving the historical mission data, performance parameters, and environmental information of the unmanned aerial vehicle, according to the preset first mathematical model, such as a neural network model, use these data to construct an ideal flight state model of the unmanned aerial vehicle. Through mathematical modeling and analysis, and combined with the current mission information of the unmanned aerial vehicle, obtain the predicted values of the ideal flight state parameters of the corresponding unmanned aerial vehicle, including attitude, speed, altitude, etc., and set the predicted values of the ideal flight state parameters of the unmanned aerial vehicle as the preset ideal flight state parameters of the unmanned aerial vehicle.

[0155] It is worth mentioning that it also includes:

[0156] Collect the historical flight parameters, environmental data, and adjustment records of the unmanned aerial vehicle, train the flight parameter optimization model of the unmanned aerial vehicle, and obtain the dynamic parameter generation strategy;

[0157] Based on the dynamic parameter generation strategy, according to the flight parameters of the unmanned aerial vehicle, output the predictive flight revision parameters, which are used to generate a pre-adjustment instruction packet;

[0158] The control end of the unmanned aerial vehicle performs flight pre-adjustment according to the pre-adjustment instruction packet, and after execution, feeds back the deviation between the actual flight state and the predicted value to the cloud for online iterative optimization of the reinforcement learning model.

[0159] It should be noted that flight parameters, environmental data, and past adjustment records are extracted from historical data stored in the cloud to construct a labeled training set. Among them, flight parameters include but are not limited to flight attitude, flight speed, and flight direction; environmental data includes but is not limited to wind speed, temperature, and humidity; past adjustment records include but are not limited to correction parameters and correction effect scores. Based on the training data set, an unmanned aerial vehicle flight parameter optimization model is trained to enable the model to learn how to generate optimal revised parameters in a specific environment; the model autonomously explores the optimal adjustment strategy to reduce the dependence on manual rules. The current flight state and environmental data of the unmanned aerial vehicle are input into the model in real time, and the model outputs the predicted revised parameters for the future time window. The cloud converts the predicted parameters into an instruction packet and sends it to the corresponding unmanned aerial vehicle. After the unmanned aerial vehicle performs pre-adjustment, it feeds back the actual flight state to the cloud for evaluating the prediction accuracy. The cloud performs online fine-tuning on the model based on the feedback data and updates the strategy to adapt to environmental changes. By using reinforcement learning to predict the flight state trend and intervening in the adjustment in advance, the lag problem of traditional reactive control is solved; and the model is iteratively optimized online to adapt to complex environmental changes.

[0160] The third aspect of the present invention provides a computer-readable storage medium, in which a program for the method of monitoring the state of an unmanned aerial vehicle for a cloud computer is stored. When the program for the method of monitoring the state of an unmanned aerial vehicle for a cloud computer is executed by a processor, the steps of the method of monitoring the state of an unmanned aerial vehicle for a cloud computer as described in any one of the above are implemented.

[0161] A method, system, and medium for monitoring the state of an unmanned aerial vehicle for a cloud computer disclosed by the present invention can, through connection with the cloud computer system, monitor the flight state of the unmanned aerial vehicle in real time and provide accurate flight parameter data. By utilizing the computing and storage capabilities of the cloud computer, a large amount of flight parameter data can be efficiently processed and analyzed. The cloud computer system can store the flight parameter data of the unmanned aerial vehicle to form historical data records, which can be used for subsequent retrospective analysis, troubleshooting, and flight mode optimization. By utilizing the historical data, the safety, reliability, and efficiency of the flight of the unmanned aerial vehicle can be further improved.

[0162] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the displayed or discussed components can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0163] The units described above as separate components may or may not be physically separated. The components displayed as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0164] In addition, each functional unit in the embodiments of the present invention can be fully integrated into a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of a combination of hardware and software functional units.

[0165] Those of ordinary skill in the art can understand that all or part of the steps to implement the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments. The foregoing storage medium includes various media that can store program codes, such as removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0166] Alternatively, if the above integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The foregoing storage medium includes various media that can store program codes, such as removable storage devices, ROM, RAM, magnetic disks, or optical discs.

Claims

1. A method for monitoring the state of an unmanned aerial vehicle for a cloud computer, characterized in that, Including: Obtaining real-time flight status parameters of an unmanned aerial vehicle; Comparing and analyzing the real-time flight status parameters of the unmanned aerial vehicle with preset ideal flight status parameters to obtain a deviation value of the flight status parameters; Judging whether the deviation value of the flight status parameters of the unmanned aerial vehicle exceeds a preset deviation threshold. If so, generating flight revision parameters for the unmanned aerial vehicle and sending the flight revision parameters for the unmanned aerial vehicle to the corresponding unmanned aerial vehicle for flight adjustment; if not, displaying on the cloud computer terminal that the flight of the unmanned aerial vehicle is normal.

2. The method for monitoring the state of an unmanned aerial vehicle for a cloud computer according to claim 1, characterized in that, The step of generating the flight status revision parameters for the unmanned aerial vehicle specifically includes: Extracting the characteristics of the flight status parameters of the unmanned aerial vehicle; Querying in a preset characteristic resistance coefficient table according to the characteristics of the flight status parameters of the unmanned aerial vehicle to obtain the resistance coefficient of the flight status parameters of the unmanned aerial vehicle corresponding to the characteristics during the current flight; Dividing the deviation value of the flight status parameters of the unmanned aerial vehicle by the corresponding resistance coefficient to obtain the current flight status revision parameters of the unmanned aerial vehicle.

3. A method for monitoring the state of an unmanned aerial vehicle for a cloud computer according to claim 1, characterized in that, Also including: Based on the flight revision parameters of the unmanned aerial vehicle, obtaining a corresponding control instruction packet for the unmanned aerial vehicle according to a preset algorithm on the cloud computer terminal; Sending the control instruction packet for the unmanned aerial vehicle to the corresponding control end of the unmanned aerial vehicle; According to the preset execution order of the control instructions for the unmanned aerial vehicle, the control end of the unmanned aerial vehicle sequentially executes the control instructions for the unmanned aerial vehicle corresponding to the control instruction packet for the unmanned aerial vehicle and feeds back the execution situation to the cloud computer terminal.

4. The method for monitoring the state of an unmanned aerial vehicle for a cloud computer according to claim 3, wherein Also including: Marking the time when the control end of the unmanned aerial vehicle receives the control instruction packet for the unmanned aerial vehicle; According to the time when the control end of the unmanned aerial vehicle receives the control instruction packet for the unmanned aerial vehicle twice adjacent, obtaining the interval duration between the time when the control end of the unmanned aerial vehicle receives the control instruction packet for the unmanned aerial vehicle twice adjacent; Judging whether the interval duration exceeds a preset first threshold of the interval duration. If so, the control end of the unmanned aerial vehicle executes the received control instruction packet for the unmanned aerial vehicle later; if not, continues to execute the previous control instruction packet for the unmanned aerial vehicle.

5. A method for monitoring the state of an unmanned aerial vehicle for a cloud computer according to claim 4, characterized in that, Also including: Obtaining the time point when the cloud computer terminal sends the control instruction packet for the unmanned aerial vehicle and the time point when it receives the feedback on the execution situation from the control end of the unmanned aerial vehicle; According to the time point when the cloud computer terminal sends the control instruction packet for the unmanned aerial vehicle and the time point when it receives the feedback on the execution situation from the control end of the unmanned aerial vehicle, obtaining the time interval between the sending of the control instruction for the unmanned aerial vehicle and the feedback on the execution situation; Judging whether the time interval between the sending of the control instruction for the unmanned aerial vehicle and the feedback on the execution situation is greater than a preset second threshold of the interval duration. If so, triggering a warning message for blocked revision and sending the flight status data corresponding to the time interval between the sending of the control instruction for the unmanned aerial vehicle and the feedback on the execution situation to the cloud computer terminal for storage.

6. A method for monitoring the state of an unmanned aerial vehicle for a cloud computer according to claim 1, characterized in that, Also including: Obtaining the number of flight adjustments of the unmanned aerial vehicle within a preset time period; Determine whether the number of flight adjustments of the unmanned aerial vehicle is greater than a preset adjustment number threshold. If so, generate an alarm message and send the alarm message to a preset client for display; if not, the corresponding unmanned aerial vehicle continues to perform flight adjustments.

7. An unmanned aerial vehicle status monitoring system for a cloud computer, characterized in that, It includes a memory and a processor. A method program for monitoring the state of an unmanned aerial vehicle for a cloud computer is stored in the memory. When the method program for monitoring the state of an unmanned aerial vehicle for a cloud computer is executed by the processor, the following steps are implemented: Obtain the real-time flight state parameters of the unmanned aerial vehicle; Compare and analyze the real-time flight state parameters of the unmanned aerial vehicle with preset ideal flight state parameters to obtain the deviation value of the flight state parameters; Determine whether the deviation value of the flight state parameters of the unmanned aerial vehicle exceeds a preset deviation threshold. If so, generate a flight revision parameter for the unmanned aerial vehicle and send the flight revision parameter for the unmanned aerial vehicle to the corresponding unmanned aerial vehicle for flight adjustment; if not, display that the flight of the unmanned aerial vehicle is normal on the cloud computer side.

8. The state monitoring system for an unmanned aerial vehicle used in a cloud computer according to claim 7, characterized in that, The step of generating the flight state revision parameter for the unmanned aerial vehicle specifically includes: Extract the characteristics of the flight state parameters of the unmanned aerial vehicle; Query in a preset characteristic resistance coefficient table according to the characteristics of the flight state parameters of the unmanned aerial vehicle to obtain the resistance coefficient of the flight state parameters of the unmanned aerial vehicle corresponding to the characteristics during the current flight; Divide the deviation value of the flight state parameters of the unmanned aerial vehicle by the corresponding resistance coefficient to obtain the current flight state revision parameter of the unmanned aerial vehicle.

9. The unmanned aerial vehicle status monitoring system for cloud computers according to claim 7, characterized in that, It also includes: Based on the flight revision parameter of the unmanned aerial vehicle, obtain a corresponding control instruction packet for the unmanned aerial vehicle according to a preset algorithm on the cloud computer side; Send the control instruction packet for the unmanned aerial vehicle to the corresponding control end of the unmanned aerial vehicle; According to the preset execution order of the unmanned aerial vehicle control instructions, the control end of the unmanned aerial vehicle sequentially executes the unmanned aerial vehicle control instructions corresponding to the control instruction packet for the unmanned aerial vehicle and feeds back the execution situation to the cloud computer side.

10. A computer-readable storage medium, characterized in that, A method program for monitoring the state of an unmanned aerial vehicle for a cloud computer is stored in the computer-readable storage medium. When the method program for monitoring the state of an unmanned aerial vehicle for a cloud computer is executed by the processor, the steps of a method for monitoring the state of an unmanned aerial vehicle for a cloud computer as described in any one of claims 1 to 6 are implemented.

Images