Universal storage system based on flight data of unmanned aerial vehicles of multiple manufacturers

Abstract

The invention provides a universal storage system based on flight data of unmanned aerial vehicles of multiple manufacturers, which relates to the technical field of unmanned aerial vehicles and comprises a data format library module, a data conversion and storage module, a real-time data processing module, a data deep mining module, a data visualization module and a flight performance analysis module. The data format library module is used for storing data format information of unmanned aerial vehicles of different manufacturers, is internally provided with a data analysis algorithm and is used for extracting key information in flight data, and the data conversion and storage module is based on the format information in the data format library module; through cooperation of the data format library module and the data conversion and storage module, original data formats of the unmanned aerial vehicles of different manufacturers are converted into a unified standard format for storage by using stored data format information of the unmanned aerial vehicles of different manufacturers and a data analysis algorithm, so that universal storage of flight data of the unmanned aerial vehicles of multiple manufacturers is realized; and the data with diversity and complexity can be conveniently and properly stored.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a universal storage system based on flight data of unmanned aerial vehicles of multiple manufacturers. Background Art

[0002] A drone is an unmanned aerial vehicle that is controlled by a radio remote control device and a self-contained program control device. From a technical point of view, drones can be divided into many types, including unmanned fixed-wing aircraft, unmanned vertical take-off and landing aircraft, unmanned airships, unmanned helicopters, unmanned multi-rotor aircraft, unmanned paragliders, etc. Drones have the advantages of small size, low cost, easy use, low requirements for the combat environment, and strong battlefield survivability. They are widely used in military reconnaissance, border patrols, nuclear radiation detection, aerial photography, agricultural and forestry monitoring, environmental monitoring, logistics and transportation, construction inspections, entertainment and competition, etc.

[0003] During the flight of a drone, flight data will be generated. Users can obtain relevant operating parameters by analyzing the flight data of the drone. However, there are many existing drone manufacturers, and the flight data of drones from different manufacturers have different formats. In the storage and analysis of flight data, a storage and analysis system specifically adapted to the manufacturer is often required, which is not universal and difficult to properly store these diverse and complex data. Therefore, the present invention proposes a universal storage system based on flight data of drones from multiple manufacturers to solve the problems existing in the prior art. Summary of the invention

[0004] In response to the above problems, the present invention proposes a universal storage system based on multi-vendor UAV flight data. The universal storage system based on multi-vendor UAV flight data realizes the universal storage of multi-vendor UAV flight data, and conveniently and properly preserves these diverse and complex data.

[0005] To achieve the purpose of the present invention, the present invention is implemented through the following technical solutions: a universal storage system based on multi-vendor UAV flight data, including a data format library module, a data conversion storage module, a real-time data processing module, a data deep mining module, a data visualization module, and a flight performance analysis module. The data format library module is used to store data format information of UAVs of different manufacturers, and has a built-in data parsing algorithm for extracting key information from flight data. The data conversion storage module converts the original data formats of UAVs of different manufacturers into a unified standard format based on the format information in the data format library module for storage;

[0006] The real-time data processing module is connected to the data conversion and storage module to process and analyze the real-time data generated during the flight of the drone, extract abnormal data and issue an alarm. The data deep mining module establishes a disaster model based on the historical data in the data conversion and storage module to predict the potential risk areas of the drone. The flight performance analysis module analyzes and judges the flight performance indicators of the drone based on the historical data in the data conversion and storage module. The data visualization module is used to access the display device to display the data of the overall system.

[0007] A further improvement is that the data format library module includes a format storage module, which is used to collect data format characteristics of different drone manufacturers, including the organization of data structures, data types, and data encoding rules, and identify the similarities and differences of data formats of various manufacturers. At the same time, the collected data format information is stored in a structured manner to form a data format library, which contains detailed descriptions of various data formats, sample data, and parsing rules.

[0008] Further improvements are: the data format library module also includes an update and maintenance module and a parsing support module. The update and maintenance module is used to establish a special update mechanism, regularly scan newly released drone products on the market and establish authorization with manufacturers to update and maintain the data format library; the parsing support module contains general data parsing algorithms and data format conversion rules, which are used to extract key information from flight data, including position, attitude, speed, and sensor data.

[0009] Further improvements are: the data conversion and storage module includes a data reading module and a data format identification module, the data reading module is used to read the original data from different drone manufacturers and identify the data content therein; the data format identification module identifies the original data format of different drone manufacturers based on the format information in the data format library module, including data structure, data type, and data encoding.

[0010] Further improvements are: the data conversion storage module also includes a rule configuration module, a conversion execution module and a correction storage unit. The rule configuration module is used to configure the conversion rules and algorithms applied in the data conversion process according to the results of the data format identification module. The conversion execution module is used to perform data conversion operations, taking the original data as input, applying the conversion rules and algorithms, and generating data in a unified standard format as output. The correction storage unit is used to verify and correct the converted data, upload it to the cloud disk for storage, synchronously timestamp it, and provide query function.

[0011] Further improvements are: the real-time data processing module includes a real-time acquisition module, a preprocessing unit, and an analysis and early warning unit. The real-time acquisition module is connected to the data conversion and storage module to collect various data generated during the flight of the drone in real time, including position data, attitude data, speed data, and sensor data; the preprocessing unit is used to clean and preprocess the real-time data, including removing duplicate data, filling missing data, and correcting erroneous data; the analysis and early warning unit is used to perform real-time analysis on the preprocessed data, including statistical analysis, trend analysis, and anomaly detection on the data, to detect abnormal situations during the flight, including abnormal flight attitude, too fast or too slow speed, and at the same time, when an abnormality is analyzed, a warning message is sent to the user, including the type, location, and time of the abnormality.

[0012] Further improvements are as follows: the data deep mining module includes a disaster model establishment module and a degradation trend analysis module. The disaster model establishment module is based on historical flight data and meteorological and geological multivariate data, and uses data mining and machine learning techniques to establish a disaster prediction model. Through model analysis, it predicts potential risk areas and the possibility of disasters. The degradation trend analysis module monitors and analyzes the flight performance parameters of the equipment based on the disaster prediction model, and analyzes the degradation trend and potential failure points of the UAV equipment by comparing multiple flight data.

[0013] Further improvements are: the data deep mining module also includes a flight log statistical analysis module and a task scheduling optimization module. The flight log statistical analysis module counts and analyzes the flight time, path accuracy and energy efficiency of the UAV based on the flight log data, and obtains the performance and usage of the UAV through statistical analysis. The task scheduling optimization module, based on the flight log statistical analysis, uses data mining and machine learning technology to optimize the task scheduling of the UAV according to the performance and usage of the UAV, with the usage efficiency and task completion quality as the direction.

[0014] Further improvements are as follows: the flight performance analysis module includes a path deviation analysis module, an energy efficiency evaluation module, a flight time analysis module, a task execution point coverage analysis module, and a flight performance optimization suggestion module. The path deviation analysis module compares the actual flight path with the planned path based on the historical flight data of the UAV, calculates the deviation value, and obtains the navigation accuracy and path control capability of the UAV during flight through deviation analysis; the energy efficiency evaluation module evaluates the energy efficiency of the UAV by calculating the ratio of battery consumption to flight distance; the flight time analysis module is used to count and analyze the flight time of the UAV, obtain the endurance and flight efficiency of the UAV, and evaluate the battery performance, flight control logic and overall design rationality of the UAV; the task execution point coverage analysis module is used to analyze the coverage of task execution points for the tasks performed by the UAV, and evaluate the accuracy and comprehensiveness of the UAV when performing tasks; the flight performance optimization suggestion module comprehensively gives flight performance optimization suggestions based on the analysis results of the path deviation analysis module, the energy efficiency evaluation module, the flight time analysis module, and the task execution point coverage analysis module, including adjusting flight parameters, optimizing flight control logic, and improving UAV design.

[0015] A further improvement is that the data visualization module is based on the Web and displays the data of the entire system to users in the form of charts, providing users with the functions of access, screening, query and analysis through a browser, including viewing the real-time flight status of the drone, historical flight trajectory, and data analysis results.

[0016] The beneficial effects of the present invention are:

[0017] 1. The present invention cooperates with a data format library module and a data conversion storage module, utilizes the stored data format information of drones from different manufacturers and a data parsing algorithm, converts the original data formats of drones from different manufacturers into a unified standard format for storage, and realizes universal storage of flight data of drones from multiple manufacturers, and conveniently and properly preserves these diverse and complex data.

[0018] 2. The present invention can quickly process and analyze the real-time data generated during the flight of the UAV, promptly discover abnormal situations during the flight, and send early warning information to the user, thereby improving the safety and stability of the flight.

[0019] 3. The present invention establishes a disaster model based on historical data, predicts potential risk areas, compares multiple flight data, analyzes the degradation trend or potential failure points of the equipment, and based on the flight log, counts the flight time, path accuracy and energy efficiency of the UAV, optimizes the scheduling of subsequent tasks, compares with the planned path based on historical data, calculates the deviation value, optimizes the flight control logic, evaluates the energy efficiency through the ratio of battery consumption to flight distance, gives low-energy optimization suggestions, analyzes indicators such as flight time and mission execution point coverage to quantify the task completion effect. In summary, it can deeply explore the potential value of data, provide strong support for the operation management and data collection of UAVs, and improve the overall performance and application level of the UAV system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a composition diagram of the present invention. DETAILED DESCRIPTION

[0021] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with examples. The examples are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0022] Embodiment 1

[0023] according to Figure 1 As shown, this embodiment proposes a universal storage system based on multi-vendor UAV flight data, including a data format library module, a data conversion and storage module, a real-time data processing module, a data deep mining module, a data visualization module, and a flight performance analysis module. The data format library module is used to store data format information of UAVs from different manufacturers, and has a built-in data parsing algorithm for extracting key information from flight data. The data conversion and storage module converts the original data formats of UAVs from different manufacturers into a unified standard format for storage based on the format information in the data format library module.

[0024] The real-time data processing module is connected to the data conversion and storage module to process and analyze the real-time data generated during the flight of the drone, extract abnormal data and issue an alarm. The data deep mining module establishes a disaster model based on the historical data in the data conversion and storage module to predict the potential risk area of ​​the drone. The flight performance analysis module analyzes and judges the flight performance indicators of the drone based on the historical data in the data conversion and storage module. The data visualization module is used to access the display device to display the data of the overall system. Study the data format characteristics of various common drone manufacturers and establish a corresponding data format library. Through in-depth analysis of the data format, develop an efficient data parsing algorithm that can accurately extract key information in the flight data, such as position, attitude, speed, sensor data, etc.

[0025] The data conversion module is designed to convert the original data formats of different manufacturers into a unified standard format. During the conversion process, the integrity and accuracy of the data are ensured while retaining the key features of the original data. This technological innovation enables data from different manufacturers to be stored and processed in the same system, which facilitates subsequent data analysis and utilization. A real-time data processing platform is built to quickly process and analyze the real-time data generated during the flight of the drone. Real-time data is collected, cleaned and analyzed in real time to promptly detect abnormal conditions during the flight and send warning information to users. Data is deeply mined to establish a disaster model based on historical data to predict potential risk areas. Compare multiple flight data to analyze the degradation trend or potential failure points of the equipment. Based on the flight log, the flight time, path accuracy and energy efficiency of the drone are counted to optimize the subsequent task scheduling. Flight performance analysis is based on historical data, compared with the planned path, the deviation value is calculated, and the flight control logic is optimized. The energy efficiency is evaluated by the ratio of battery consumption to flight distance, and low-energy optimization suggestions are given. Indicators such as flight time and task execution point coverage are analyzed to quantify the task completion effect.

[0026] The data format library module includes a format storage module, an update and maintenance module, and a parsing support module. The format storage module is used to collect the data format characteristics of different drone manufacturers, including the organization of data structure, data type, data encoding rules, and identify the similarities and differences of the data formats of each manufacturer, providing theoretical support for the subsequent construction of the data format library. At the same time, the collected data format information is stored in a structured manner to form a data format library, which contains detailed descriptions of various data formats, sample data, and parsing rules. The update and maintenance module is used to establish a special update mechanism, regularly scan the newly released drone products in the market and establish authorization with the manufacturer to update and maintain the data format library; the parsing support module contains a general data parsing algorithm and data format conversion rules, which are used to extract key information from flight data, including position, attitude, speed, and sensor data. Specifically, reverse engineering, document analysis, communication with manufacturers, and other methods can be used to obtain and understand the detailed information of drone data formats. A database management system (such as MySQL, PostgreSQL, etc.) or a special data format library management system is used to store and manage these data format information. The continuous update and maintenance of data formats is achieved by establishing a special update mechanism (such as regularly scanning the newly released drone products in the market, establishing long-term cooperative relationships with manufacturers, etc.). Provide necessary support and assistance for data analysis by providing API interfaces, code libraries, documents, etc.

[0027] The data conversion storage module includes a data reading module, a data format identification module, a rule configuration module, a conversion execution module and a correction storage unit. The data reading module is used to read the original data from different drone manufacturers and identify the data content therein; the data format identification module identifies the original data format of different drone manufacturers based on the format information in the data format library module, including data structure, data type, and data encoding. The rule configuration module is used to configure the conversion rules and algorithms used in the data conversion process according to the results of the data format identification module. The conversion execution module is used to perform data conversion operations, taking the original data as input, applying the conversion rules and algorithms, and generating data in a unified standard format as output. The correction storage unit is used to verify and correct the converted data, upload it to the cloud disk for storage, and synchronously timestamp it to provide query functions. Specifically, data reading can be achieved by using file parsing library, database connection and other methods; data format identification can be achieved by comparing information in the data format library, using regular expressions, parsing data headers and other methods; conversion rules can be configured through configuration files, database queries, code generation and other methods; data conversion can be achieved by writing conversion logic code, calling conversion library functions and other methods; data verification and correction can be achieved through data verification rules, exception handling mechanisms, data cleaning algorithms and other methods.

[0028] The real-time data processing module includes a real-time acquisition module, a preprocessing unit, and an analysis and warning unit. The real-time acquisition module is connected to the data conversion and storage module to collect various data generated during the flight of the drone in real time, including position data, attitude data, speed data, and sensor data; the preprocessing unit is used to clean and preprocess the real-time data, including removing duplicate data, filling missing data, and correcting erroneous data; the analysis and warning unit is used to perform real-time analysis on the preprocessed data, including statistical analysis, trend analysis, and anomaly detection on the data, and find abnormal conditions during the flight, including abnormal flight attitude, too fast or too slow speed. At the same time, when the abnormality is analyzed, a warning message is sent to the user, including the type, location, and time of the abnormality. Specifically, special data acquisition tools, API interfaces, data flow technologies, etc. can be used to realize real-time data acquisition; data cleaning tools, regular expressions, data filtering algorithms, etc. can be used to realize data cleaning and preprocessing; real-time data analysis tools, machine learning algorithms, data mining technologies, etc. are used to realize real-time data analysis; SMS, email, APP push, etc. are used to realize the sending of warning information.

[0029] The data deep mining module includes a disaster model building module, a degradation trend analysis module, a flight log statistical analysis module, and a task scheduling optimization module. The disaster model building module uses data mining and machine learning technology to build a disaster prediction model based on historical flight data and meteorological and geological multivariate data, and predicts potential risk areas and the possibility of disasters through model analysis; the degradation trend analysis module monitors and analyzes the flight performance parameters of the equipment based on the disaster prediction model, and analyzes the degradation trend and potential fault points of the drone equipment by comparing multiple flight data. The flight log statistical analysis module counts and analyzes the flight time, path accuracy and energy efficiency of the drone based on the flight log data, and obtains the performance and usage of the drone through statistical analysis. The task scheduling optimization module uses data mining and machine learning technology based on the flight log statistical analysis, and optimizes the task scheduling of the drone based on the performance and usage of the drone, with the efficiency of use and the quality of task completion as the direction. Specifically, machine learning algorithms such as decision trees, random forests, and support vector machines can be used in combination with geographic information systems (GIS) and remote sensing technology to achieve the establishment and prediction of disaster models; time series analysis, cluster analysis, anomaly detection and other technologies can be used to conduct in-depth mining and analysis of flight data, so as to achieve accurate prediction of equipment degradation trends; database query, data visualization and other technologies can be used to quickly process and display flight log data, so as to achieve intuitive analysis and evaluation of flight performance; optimization algorithms such as genetic algorithms and reinforcement learning can be used in combination with the performance parameters and mission requirements of drones to achieve intelligent optimization of task scheduling; data visualization tools (such as Tableau, Power BI, etc.) and report generation tools (such as Word, Excel, etc.) can be used to present the mining results to users in an intuitive and easy-to-understand manner.

[0030] The flight performance analysis module includes a path deviation analysis module, an energy efficiency evaluation module, a flight time analysis module, a task execution point coverage analysis module, and a flight performance optimization suggestion module. The path deviation analysis module compares the actual flight path with the planned path based on the historical flight data of the UAV, calculates the deviation value, and obtains the navigation accuracy and path control ability of the UAV during the flight through deviation analysis; the energy efficiency evaluation module evaluates the energy efficiency of the UAV by calculating the ratio of battery consumption to flight distance; the flight time analysis module is used to count and analyze the flight time of the UAV, obtain the endurance and flight efficiency of the UAV, and evaluate the battery performance, flight control logic and overall design rationality of the UAV; the task execution point coverage analysis module is used to analyze the coverage of task execution points for the tasks performed by the UAV, and evaluate the accuracy and comprehensiveness of the UAV when performing the tasks; the flight performance optimization suggestion module comprehensively gives flight performance optimization suggestions based on the analysis results of the path deviation analysis module, the energy efficiency evaluation module, the flight time analysis module, and the task execution point coverage analysis module, including adjusting flight parameters, optimizing flight control logic, and improving UAV design. The geographic information system (GIS) and path planning algorithm are used to accurately calculate and compare flight paths to obtain deviation values. Data fitting, regression analysis and other technologies are used to model and analyze the relationship between battery consumption and flight distance to obtain energy efficiency evaluation results. Time series analysis, data visualization and other technologies are used to process and display flight time data to obtain flight time analysis results. Spatial analysis, data mining and other technologies are used to count and analyze the location and number of mission execution points to obtain coverage analysis results. Intelligent optimization algorithms, expert systems and other technologies are used to comprehensively evaluate the analysis results and generate optimization suggestions.

[0031] Embodiment 2

[0032] according to Figure 1 As shown, this embodiment proposes a universal storage system based on multi-vendor UAV flight data, including a data format library module, a data conversion and storage module, a real-time data processing module, a data deep mining module, a data visualization module, and a flight performance analysis module. The data format library module is used to store data format information of UAVs from different manufacturers, and has a built-in data parsing algorithm for extracting key information from flight data. The data conversion and storage module converts the original data formats of UAVs from different manufacturers into a unified standard format for storage based on the format information in the data format library module.

[0033] The real-time data processing module is connected to the data conversion and storage module to process and analyze the real-time data generated during the flight of the drone, extract anomalies, and issue an alarm. The data deep mining module establishes a disaster model based on the historical data in the data conversion and storage module to predict the potential risk area of ​​the drone. The flight performance analysis module analyzes and judges the flight performance indicators of the drone based on the historical data in the data conversion and storage module. The data visualization module is used to access the display device to display the data of the overall system. The data format characteristics of various common drone manufacturers are studied and the corresponding data format library is established. Through in-depth analysis of the data format, an efficient data parsing algorithm is developed to accurately extract key information in the flight data, such as position, attitude, speed, sensor data, etc. The data conversion module is designed to convert the original data formats of different manufacturers into a unified standard format. During the conversion process, the integrity and accuracy of the data are ensured while retaining the key features of the original data. This technological innovation enables data from different manufacturers to be stored and processed in the same system, which facilitates subsequent data analysis and utilization. A real-time data processing platform is built to quickly process and analyze the real-time data generated during the flight of the drone. Real-time data is collected, cleaned and analyzed in real time, abnormal conditions during the flight are discovered in a timely manner, and early warning information is sent to users. Deep data mining, build disaster models based on historical data, and predict potential risk areas. Compare multiple flight data to analyze equipment degradation trends or potential failure points. Based on flight logs, count the flight time, path accuracy, and energy efficiency of the drone to optimize subsequent task scheduling. Flight performance analysis, based on historical data, compare with the planned path, calculate deviation values, and optimize flight control logic. Evaluate energy efficiency through the ratio of battery consumption to flight distance, and give low-energy optimization suggestions. Analyze indicators such as flight time and mission execution point coverage to quantify the effectiveness of mission completion.

[0034] The data visualization module is based on the Web and displays the data of the entire system to the user in the form of charts, providing users with the functions of access, filtering, querying and analyzing through a browser, including viewing the real-time flight status, historical flight trajectory, and data analysis results of the drone. Develop a Web-based data visualization system to display the processed data to users in the form of intuitive and easy-to-understand charts. Users can access the visualization system anytime and anywhere through a browser to view information such as the real-time flight status, historical flight trajectory, and data analysis results of the drone. The visualization system supports a variety of interactive operations, and users can filter, query and analyze data according to their needs. The data visualization module enables users to intuitively view and analyze drone flight data, improving the readability and ease of use of the data.

[0035] This universal storage system based on multi-vendor UAV flight data cooperates with a data format library module and a data conversion storage module. It uses the stored data format information of UAVs from different manufacturers and data parsing algorithms to convert the original data formats of UAVs from different manufacturers into a unified standard format for storage, thereby realizing universal storage of multi-vendor UAV flight data and conveniently and properly preserving these diverse and complex data.

[0036] The present invention can quickly process and analyze the real-time data generated during the flight of the drone, timely discover abnormal situations during the flight, and send warning information to the user to improve the safety and stability of the flight. At the same time, the present invention establishes a disaster model based on historical data, predicts potential risk areas, compares multiple flight data, analyzes the degradation trend or potential fault points of the equipment, and based on the flight log, counts the flight time, path accuracy and energy efficiency of the drone, optimizes the scheduling of subsequent tasks, compares with the planned path based on historical data, calculates the deviation value, optimizes the flight control logic, evaluates the energy efficiency through the ratio of battery consumption to flight distance, gives low-energy optimization suggestions, analyzes flight time, task execution point coverage and other indicators to quantify the task completion effect, and in summary, can deeply explore the potential value of data, provide strong support for the operation management and data collection of drones, and improve the overall performance and application level of drone systems.

[0037] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A universal storage system based on multi-vendor UAV flight data, including a data format library module, a data conversion and storage module, a real-time data processing module, a data deep mining module, a data visualization module, and a flight performance analysis module, characterized by: The data format library module is used to store data format information of drones from different manufacturers, and has a built-in data parsing algorithm for extracting key information from flight data. The data conversion and storage module converts the original data formats of drones from different manufacturers into a unified standard format based on the format information in the data format library module for storage; The real-time data processing module is connected to the data conversion and storage module to process and analyze the real-time data generated during the flight of the drone, extract abnormal data and issue an alarm. The data deep mining module establishes a disaster model based on the historical data in the data conversion and storage module to predict the potential risk areas of the drone. The flight performance analysis module analyzes and judges the flight performance indicators of the drone based on the historical data in the data conversion and storage module. The data visualization module is used to access the display device to display the data of the overall system.

2. The universal storage system based on multi-vendor UAV flight data according to claim 1 is characterized in that: The data format library module includes a format storage module, which is used to collect data format characteristics of different drone manufacturers, including the organization of data structures, data types, and data encoding rules, and identify the similarities and differences between the data formats of each manufacturer. At the same time, the collected data format information is stored in a structured manner to form a data format library, which includes detailed descriptions of various data formats, sample data, and parsing rules.

3. The universal storage system based on multi-vendor UAV flight data according to claim 1 is characterized in that: The data format library module includes an update and maintenance module and a parsing support module. The update and maintenance module is used to establish a special update mechanism, regularly scan newly released drone products on the market and establish authorization with manufacturers to update and maintain the data format library; the parsing support module contains general data parsing algorithms and data format conversion rules, which are used to extract key information from flight data, including position, attitude, speed, and sensor data.

4. The universal storage system based on multi-vendor UAV flight data according to claim 1 is characterized in that: The data conversion and storage module includes a data reading module and a data format identification module. The data reading module is used to read the original data from different drone manufacturers and identify the data content therein; the data format identification module identifies the original data format of different drone manufacturers based on the format information in the data format library module, including data structure, data type, and data encoding.

5. The universal storage system based on multi-vendor UAV flight data according to claim 4 is characterized in that: The data conversion storage module also includes a rule configuration module, a conversion execution module and a correction storage unit. The rule configuration module is used to configure the conversion rules and algorithms applied in the data conversion process according to the results of the data format identification module. The conversion execution module is used to perform data conversion operations, taking the original data as input, applying the conversion rules and algorithms, and generating data in a unified standard format as output. The correction storage unit is used to verify and correct the converted data, upload it to the cloud disk for storage, synchronously timestamp it, and provide query function.

6. The universal storage system based on multi-vendor UAV flight data according to claim 1 is characterized in that: The real-time data processing module includes a real-time acquisition module, a preprocessing unit, and an analysis and warning unit. The real-time acquisition module is connected to the data conversion and storage module to collect various data generated during the flight of the drone in real time, including position data, attitude data, speed data, and sensor data; The preprocessing unit is used to clean and preprocess the real-time data, including removing duplicate data, filling missing data, and correcting erroneous data; The analysis and warning unit is used to perform real-time analysis on the pre-processed data, including statistical analysis, trend analysis, and anomaly detection on the data, to detect abnormal conditions during the flight, including abnormal flight attitude, too fast or too slow speed. At the same time, when an abnormality is analyzed, a warning message is sent to the user, including the type, location, and time of the abnormality.

7. The universal storage system based on multi-vendor UAV flight data according to claim 1 is characterized in that: The data deep mining module includes a disaster model establishment module and a degradation trend analysis module. The disaster model establishment module establishes a disaster prediction model based on historical flight data and meteorological and geological multivariate data, and uses data mining and machine learning techniques to predict potential risk areas and the possibility of disasters through model analysis; the degradation trend analysis module monitors and analyzes the flight performance parameters of the equipment based on the disaster prediction model, and analyzes the degradation trend and potential failure points of the UAV equipment by comparing multiple flight data.

8. The universal storage system based on multi-vendor UAV flight data according to claim 1 is characterized in that: The data deep mining module also includes a flight log statistical analysis module and a task scheduling optimization module. The flight log statistical analysis module counts and analyzes the flight time, path accuracy and energy efficiency of the UAV based on the flight log data, and obtains the performance and usage of the UAV through statistical analysis. The task scheduling optimization module, based on the flight log statistical analysis, uses data mining and machine learning technology to optimize the task scheduling of the UAV according to the performance and usage of the UAV, with efficiency and task completion quality as the direction.

9. The universal storage system based on multi-vendor UAV flight data according to claim 1 is characterized in that: The flight performance analysis module includes a path deviation analysis module, an energy efficiency evaluation module, a flight time analysis module, a task execution point coverage analysis module, and a flight performance optimization suggestion module. The path deviation analysis module compares the actual flight path with the planned path based on the historical flight data of the UAV, calculates the deviation value, and obtains the navigation accuracy and path control ability of the UAV during the flight through deviation analysis; the energy efficiency evaluation module evaluates the energy efficiency of the UAV by calculating the ratio of battery consumption to flight distance; the flight time analysis module is used to count and analyze the flight time of the UAV, obtain the endurance and flight efficiency of the UAV, and evaluate the battery performance, flight control logic and overall design rationality of the UAV; the task execution point coverage analysis module is used to analyze the coverage of task execution points for the tasks performed by the UAV, and evaluate the accuracy and comprehensiveness of the UAV when performing the tasks; the flight performance optimization suggestion module comprehensively gives flight performance optimization suggestions based on the analysis results of the path deviation analysis module, the energy efficiency evaluation module, the flight time analysis module, and the task execution point coverage analysis module, including adjusting flight parameters, optimizing flight control logic, and improving UAV design.

10. The universal storage system based on multi-vendor UAV flight data according to claim 1 is characterized in that: The data visualization module is based on the Web and displays the data of the entire system to users in the form of charts, providing users with the functions of access, screening, query and analysis through a browser, including viewing the real-time flight status, historical flight trajectory and data analysis results of the drone.

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