A visual management system for UAV routes

Abstract

The present invention provides a visual management system for unmanned aerial vehicle (UAV) routes, belonging to the technical field of UAV visual management. The system comprises: a collection module that obtains the model of a target UAV, identifies the corresponding sensor, and collects the UAV's flight data in real time; a first determination module that processes the flight data and determines the UAV's optimal route based on a path planning algorithm; a dynamic adjustment module that obtains environmental parameters surrounding the UAV and dynamically adjusts the UAV's optimal route; a display module that visualizes the UAV's flight trajectory and meteorological conditions using GIS technology to monitor the UAV's dynamic flight status; and a trigger module that sets safety thresholds based on the UAV's performance and real-time environmental conditions, triggers alarms based on the safety thresholds, and implements visual management of the UAV's routes based on the alarm information. This system solves the problems of inaccurate route planning, poor visualization, and the inability to guarantee real-time flight data acquisition due to manual operation.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) visual management, and in particular to a UAV route visual management system. Background Art

[0002] With the development of my country's economy, the demand for transportation is increasing. Among various means of transportation, drones, as a new mode of transportation, have received extensive attention and research. Traditional drone route visualization management systems require manual operation, which can easily lead to untimely and inaccurate route planning. Especially in complex environments, this delay and inaccuracy can cause serious flight accidents. Furthermore, traditional drone route visualization management systems often have poor visualization effects, unable to clearly reflect the actual position and speed of the drone, and unable to guarantee the real-time acquisition of flight data, resulting in the lack of timely warnings and the risk of flight hazards.

[0003] Therefore, the present invention proposes a UAV route visualization management system. Summary of the Invention

[0004] The present invention provides a UAV route visualization management system to solve the problem that the existing technology requires manual operation, which easily leads to untimely and inaccurate route planning. Especially in complex environments, such untimely and inaccurate planning may cause serious flight accidents. At the same time, the visualization effect of traditional UAV route visualization management systems is usually poor, and the actual position and speed of the UAV cannot be clearly reflected. The real-time acquisition of flight data cannot be guaranteed, resulting in the inability to provide timely warnings and the occurrence of flight hazards.

[0005] In one aspect, the present invention provides a UAV route visualization management system, comprising:

[0006] Collection module: obtains the model of the target drone, determines the corresponding sensor based on the model, and collects the drone's flight data in real time based on the sensor;

[0007] A first determination module processes the flight data using data fusion technology, and determines the optimal route of the UAV based on the processing results and a path planning algorithm;

[0008] Dynamic adjustment module: obtains the real-time surrounding environment parameters of the target UAV and dynamically adjusts the optimal route of the UAV according to the real-time surrounding environment parameters;

[0009] Display module: Based on GIS technology, the adjusted flight trajectory of the UAV and weather conditions are visualized and updated in real time to obtain dynamic flight situation monitoring of the UAV;

[0010] Trigger module: Sets safety thresholds based on the UAV's performance and real-time environmental conditions based on the UAV's dynamic flight situation monitoring, triggers alarms based on the safety thresholds, and implements visual management of the UAV's route based on the alarm information.

[0011] According to the present invention, a UAV route visualization management system and a collection module are provided, including:

[0012] A first determining unit is configured to obtain structural and appearance characteristics of a target UAV and determine the model of the target UAV based on the structural and appearance characteristics.

[0013] A first acquiring unit: determining sensor specifications and compatibility requirements according to the model, and acquiring a corresponding sensor based on the sensor specifications and compatibility requirements;

[0014] Configuration unit: Configure the sensor interface and communication protocol according to the product manual to obtain real-time flight data of the drone.

[0015] According to the present invention, a UAV route visualization management system includes a first determination module, which includes:

[0016] Data fusion unit: fuses the flight data acquired by different sensors based on fusion algorithms;

[0017] The first generation unit generates the flight path points and flight path of the UAV based on the fused data and pre-defined navigation rules;

[0018] Comparison unit: uses the fused data to determine the distance between each adjacent path point, compares the length of each path based on the distance between each path point, and obtains the comparison result;

[0019] The second acquisition unit: obtains the optimal route of the UAV based on the comparison result and the obstacle avoidance algorithm to minimize the path length and the time to avoid obstacles.

[0020] According to the present invention, a UAV route visualization management system and a dynamic adjustment module are provided, including:

[0021] The third acquisition unit: acquires the real-time surrounding environment parameters of the target UAV based on sensors related to environmental perception;

[0022] Identification and filtering unit: identifies and filters the surrounding environment parameters to obtain key environmental parameters;

[0023] Adjustment unit: adjusts the flight mode and safety parameters of the drone based on the heuristic algorithm according to the pre-set destination information and current environmental conditions;

[0024] Dynamic adjustment unit: dynamically adjusts the optimal route of the drone based on the adjustment results.

[0025] According to the present invention, a UAV route visualization management system and display module are provided, including:

[0026] A second determining unit is configured to obtain an adjusted flight trajectory of the UAV based on the fuel consumption and time delay of the UAV, and determine the position information of the UAV based on the flight trajectory;

[0027] The second generation unit is configured to obtain real-time changes in meteorological conditions and generate a real-time UAV mission plan based on the UAV's location information;

[0028] Display unit: Generate a visual map based on GIS technology according to the UAV mission plan for visual display and update in real time;

[0029] The fourth acquisition unit generates a real-time task scheduling suggestion according to the update result, and obtains the dynamic flight situation monitoring of the UAV according to the task scheduling suggestion.

[0030] According to the present invention, a UAV route visualization management system and a trigger module are provided, including:

[0031] The fifth acquisition unit: obtains the performance data and limitation parameters of the drone according to the drone documentation;

[0032] The sixth acquisition unit is configured to acquire the real-time environmental condition parameters of the UAV based on multiple devices of the UAV;

[0033] A third determining unit: establishing a model based on a machine learning algorithm according to the performance parameters of the UAV and the real-time environmental condition parameters to determine the optimal flight parameters of the UAV;

[0034] Setting unit: setting a safety threshold for UAV flight based on the optimal flight parameters of the UAV and dynamic flight situation monitoring of the UAV;

[0035] A judgment unit: judging the real-time flight status and real-time flight parameters of the UAV according to the safety threshold of the UAV flight;

[0036] Extraction unit: If the judgment result exceeds the safety threshold, an alarm is triggered, key feature information in the alarm information is extracted, and visual management of the UAV route is achieved based on the key feature information.

[0037] A UAV route visualization management system according to the present invention also includes:

[0038] Query module: determines the corresponding operation interface according to the user's needs and usage habits, and conducts multi-angle and multi-form queries based on the operation interface;

[0039] Permission authentication module: obtains the identity information of the querying user, performs permission authentication based on the identity information, and determines whether the user is an internal user of the data access object based on the authentication result. If not, obtains the external user access security policy in the data access scenario;

[0040] The second determination module: determines the data description parameters within the user's access permission range according to the external user access security policy;

[0041] Retrieval module: retrieves relevant permission data from the data repository of the data access object according to the data description parameters and the user's type of data to be retrieved.

[0042] According to the present invention, a visual management system for a UAV route is provided, wherein the setting unit includes:

[0043] The first determination subunit: obtains the flight area review parameters of the UAV, and determines the macro flight situation index set and the micro flight situation index set of the UAV based on the review parameters;

[0044] Generating subunit: generating a flight point sequence of the UAV based on the UAV's optimal flight parameters and the UAV's macroscopic flight situation index set and microscopic flight situation index set;

[0045] The first acquisition subunit: maps the drone's flight point sequence and obtains the drone's flight status heat map;

[0046] The second determination subunit: determines the flight altitude and flight attitude information of the UAV according to the flight status thermal map;

[0047] The third determining subunit determines the flight threat source information and flight power source information of the UAV based on the flight altitude information and flight attitude information combined with the environmental parameter information of the flight area;

[0048] Establish subunits: establish threat situation quantification matrix and power situation quantification matrix based on flight threat source information and flight power source information;

[0049] The fourth determination subunit: determines multiple flight decision indicators and specific indicator reference values ​​through the threat situation quantification matrix and the power situation quantification matrix;

[0050] Setting subunit: Set the safety threshold of UAV flight based on flight decision indicators and specific indicator reference values.

[0051] Compared with the prior art, the present invention has the following advantages:

[0052] By determining the optimal route of the drone based on the path planning algorithm based on the flight data of the drone, dynamically adjusting the optimal route, and visually displaying it, and issuing warnings based on the performance of the drone and real-time environmental conditions, it avoids serious flight accidents caused by untimely and inaccurate route planning caused by manual operation. At the same time, it improves the visualization effect of the visual management system, clearly reflects the actual position and speed information of the drone, ensures the real-time acquisition of flight data, and issues timely warnings to avoid flight hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 This is one of the structural diagrams of the UAV route visualization management system provided by an embodiment of the present invention;

[0055] Figure 2 It is a schematic diagram of the process structure of the collection module provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0057] Example 1:

[0058] The embodiment of the present invention provides a UAV route visualization management system, such as Figure 1 As shown, the system mainly includes the following modules:

[0059] Collection module: obtains the model of the target drone, determines the corresponding sensor based on the model, and collects the drone's flight data in real time based on the sensor;

[0060] A first determination module processes the flight data using data fusion technology, and determines the optimal route of the UAV based on the processing results and a path planning algorithm;

[0061] Dynamic adjustment module: obtains the real-time surrounding environment parameters of the target UAV and dynamically adjusts the optimal route of the UAV according to the real-time surrounding environment parameters;

[0062] Display module: Based on GIS technology, the adjusted flight trajectory of the UAV and weather conditions are visualized and updated in real time to obtain dynamic flight situation monitoring of the UAV;

[0063] Trigger module: Sets safety thresholds based on the UAV's performance and real-time environmental conditions based on the UAV's dynamic flight situation monitoring, triggers alarms based on the safety thresholds, and implements visual management of the UAV's route based on the alarm information.

[0064] In this embodiment, the sensor is a device that can sense and measure various environmental quantities, including: a gyroscope, an accelerometer, an altimeter, a barometer, and an electronic compass.

[0065] In this embodiment, the flight data of the UAV includes: aircraft attitude changes, aircraft acceleration changes, air pressure, temperature, altitude, and direction.

[0066] In this embodiment, data fusion technology refers to a technology that integrates and processes information from multiple data sources to obtain more accurate and reliable decision support and information extraction, such as: Kalman filter, Bayesian network, neural network.

[0067] In this embodiment, the path planning algorithm is an algorithm for solving the shortest path problem from an initial position to a target position in an unknown environment, such as the Dijkstra algorithm and the A* algorithm.

[0068] In this embodiment, the optimal route of the drone refers to the optimal route that the drone should fly when performing a specific mission.

[0069] In this embodiment, the real-time environmental parameters of the drone include: altitude, wind speed and direction, positioning, and temperature.

[0070] In this embodiment, dynamic adjustment refers to real-time monitoring of the flight parameters, attitude, etc. of the drone during flight and automatic adjustment based on actual conditions, including adjustments in speed control, position maintenance, and attitude stabilization.

[0071] In this embodiment, GIS technology, namely geographic information system technology, is a technical system for collecting, storing, managing, calculating, analyzing, displaying and describing geographic spatial data through computer hardware, software and data sharing.

[0072] In this embodiment, the flight trajectory refers to the route that the drone travels from takeoff to landing, and can be drawn by recording the GPS positioning point and flight time of the drone.

[0073] In this embodiment, meteorological conditions refer to various weather phenomena and atmospheric conditions that affect aircraft flight in a specific area and time period, such as visibility, wind direction and speed, cloud cover, and atmospheric pressure.

[0074] In this embodiment, the dynamic flight status monitoring of the UAV refers to real-time monitoring of the UAV's flight status through a remote controller or a ground station to ensure that the UAV is always within a controllable range during flight.

[0075] In this embodiment, the performance of the UAV includes: flight altitude, flight speed, wind resistance, and control radius.

[0076] In this embodiment, the real-time environmental conditions refer to various parameters of the current surrounding environment, such as temperature, humidity, air pressure, wind direction, and wind speed.

[0077] In this embodiment, the safety threshold is a range set during the design and operation of the drone to ensure that the drone can operate normally and safely under various weather conditions and operating conditions. For example, the maximum flight altitude is 1,000 meters. If it exceeds 1,000 meters, an early warning alert will be issued.

[0078] The beneficial effects of the above technical solution are: by determining the optimal route of the drone based on the path planning algorithm based on the flight data of the drone, and dynamically adjusting the optimal route, visually displaying it, and issuing early warnings based on the performance of the drone and real-time environmental conditions, serious flight accidents caused by untimely and inaccurate route planning caused by manual operation are avoided. At the same time, the visualization effect of the visualization management system is improved, and the actual position and speed information of the drone is clearly reflected, ensuring the real-time acquisition of flight data, and timely warnings are issued to avoid flight hazards.

[0079] Example 2:

[0080] Based on Example 1, the collection module of the embodiment of the present invention is as follows: Figure 2 As shown, including:

[0081] A first determining unit is configured to obtain structural and appearance characteristics of a target UAV and determine the model of the target UAV based on the structural and appearance characteristics.

[0082] A first acquiring unit: determining sensor specifications and compatibility requirements according to the model, and acquiring a corresponding sensor based on the sensor specifications and compatibility requirements;

[0083] Configuration unit: Configure the sensor interface and communication protocol according to the product manual to obtain real-time flight data of the drone.

[0084] In this embodiment, the structure of the UAV includes: a flight controller, a power system, a navigation system, and a remote controller.

[0085] In this embodiment, the appearance features of the drone include: fuselage material, rotor and tail, sensor cabin, and electronic equipment cabin.

[0086] In this embodiment, the sensor specifications refer to its performance parameters on the drone, including:

[0087] Resolution: refers to the minimum resolvable size of the sensor. For example, camera resolution can be divided into several percentage levels. For example, 4K means that 4096 points (pixels) can be distinguished in every inch of visible area.

[0088] Frame rate: This refers to how many photos or videos can be taken per second. A higher frame rate can make the movement smoother.

[0089] Distance measurement accuracy: This term describes how accurately a sensor measures distance. For example, using HDR technology can improve distance measurement accuracy.

[0090] Field of view angle: refers to the range that the sensor can see on the horizontal plane. The larger the viewing angle, the wider the field of view.

[0091] Anti-interference: refers to the ability of the sensor to continue to work normally when encountering obstructions or electromagnetic interference.

[0092] In this embodiment, the compatibility requirements include: integration with the UAV system and compatibility with the ground station.

[0093] In this embodiment, the sensor interface refers to a channel for data exchange or communication between the sensor and other electronic devices or systems.

[0094] In this embodiment, the communication protocol is the agreement or specification followed when the drone interacts with the ground station, other unmanned systems, and various sensors. It defines the format, encoding method, and synchronization method of the data, such as the RC protocol and the FUTABA protocol.

[0095] The beneficial effects of the above technical solution are: determining the corresponding sensor according to the sensor specifications and compatibility requirements of the drone model, and obtaining the real-time flight data of the drone, can ensure that all sensors follow the same standards and specifications, avoiding equipment waste or functional limitations due to interface or protocol incompatibility, and further avoiding the problem of confusing and inaccurate data acquisition.

[0096] Example 3:

[0097] Based on Example 2, the first determination module in this embodiment of the present invention includes:

[0098] Data fusion unit: fuses the flight data acquired by different sensors based on fusion algorithms;

[0099] The first generation unit generates the flight path points and flight path of the UAV based on the fused data and pre-defined navigation rules;

[0100] Comparison unit: uses the fused data to determine the distance between each adjacent path point, compares the length of each path based on the distance between each path point, and obtains the comparison result;

[0101] The second acquisition unit: obtains the optimal route of the UAV based on the comparison result and the obstacle avoidance algorithm to minimize the path length and the time to avoid obstacles.

[0102] In this embodiment, data fusion technology refers to a technology that integrates and processes information from multiple data sources to obtain more accurate and reliable decision support and information extraction, such as: Kalman filter, Bayesian network, neural network.

[0103] In this embodiment, the predefined navigation rules refer to rules set in advance during the UAV's flight mission to ensure that the UAV flies autonomously along a predetermined trajectory, such as:

[0104] Waypoint planning: The mission area is divided into several fixed waypoints. The drone needs to complete the scanning and landing tasks of these waypoints in sequence within the specified time and fuel consumption.

[0105] Obstacle avoidance strategy: During the navigation process of the drone, it is necessary to take into account the obstacles in the surrounding environment, such as buildings, trees, etc., to avoid collisions with them.

[0106] Time limit: Set a reasonable operating time range for the drone to ensure that the mission can be completed within the specified time limit.

[0107] In this embodiment, the flight path points of the drone refer to a series of predetermined paths followed by the drone when performing a flight mission.

[0108] In this embodiment, the obstacle avoidance algorithm is a technology for autonomous navigation of a UAV, and its main purpose is to enable the UAV to avoid collisions with other objects during flight, such as: visual obstacle avoidance algorithm, lidar obstacle avoidance algorithm.

[0109] The beneficial effects of the above technical solution are: generating the flight path points and flight path of the drone based on the fused flight data combined with pre-defined navigation rules, comparing the length of each path according to the distance of each path point, and obtaining the optimal route based on the obstacle avoidance algorithm, which can enable the drone to avoid obstacles and other risk factors, thereby improving the safety of flight. At the same time, determining the optimal route can improve the flight efficiency of the drone.

[0110] Example 4:

[0111] Based on Example 3, the dynamic adjustment module of this embodiment of the present invention includes:

[0112] The third acquisition unit: acquires the real-time surrounding environment parameters of the target UAV based on sensors related to environmental perception;

[0113] Identification and filtering unit: identifies and filters the surrounding environment parameters to obtain key environmental parameters;

[0114] Adjustment unit: adjusts the flight mode and safety parameters of the drone based on the heuristic algorithm according to the pre-set destination information and current environmental conditions;

[0115] Dynamic adjustment unit: dynamically adjusts the optimal route of the drone based on the adjustment results.

[0116] In this embodiment, sensors related to environmental perception include: distance sensor, lidar, camera, and barometer.

[0117] In this embodiment, the environmental parameters include: altitude, speed, direction, and weather.

[0118] In this embodiment, the heuristic algorithm refers to a type of algorithm that is not guaranteed to be absolutely correct but can usually obtain a satisfactory solution.

[0119] In this embodiment, the flight mode of the drone refers to the behavior of the drone in different states in the air, such as autonomous flight mode, remote control mode, and autonomous navigation mode.

[0120] In this embodiment, the safety parameters of the drone are important indicators for evaluating the safety performance of the drone, such as flight speed, battery life, and obstacle avoidance capability.

[0121] The beneficial effects of the above technical solution are: the flight mode and safety parameters of the drone are adjusted based on the heuristic algorithm according to the pre-set destination information and current environmental conditions, so as to dynamically adjust the optimal route of the drone. The flight path of the drone can be adjusted in real time according to the actual environmental conditions, and the environmental changes can be responded to in real time, making the drone flight more flexible and adaptable, thereby improving flight safety.

[0122] Example 5:

[0123] Based on Example 4, the display module of this embodiment of the present invention includes:

[0124] A second determining unit is configured to obtain an adjusted flight trajectory of the UAV based on the fuel consumption and time delay of the UAV, and determine the position information of the UAV based on the flight trajectory;

[0125] The second generation unit is configured to obtain real-time changes in meteorological conditions and generate a real-time UAV mission plan based on the UAV's location information;

[0126] Display unit: Generate a visual map based on GIS technology according to the UAV mission plan for visual display and update in real time;

[0127] The fourth acquisition unit generates a real-time task scheduling suggestion according to the update result, and obtains the dynamic flight situation monitoring of the UAV according to the task scheduling suggestion.

[0128] In this embodiment, time delay refers to the situation where the UAV fails to complete the flight mission on time due to various reasons.

[0129] In this embodiment, the location information of the UAV refers to data for determining the geographical location of the UAV during flight, including longitude, latitude, and altitude information.

[0130] In this embodiment, UAV mission planning refers to specifying a specific flight plan for the UAV to ensure that it can complete the intended mission safely and efficiently.

[0131] In this embodiment, the task scheduling suggestion refers to a suggestion for formulating a detailed action plan for the drone when performing a specific task, such as determining the mission type and purpose of the drone, and determining the operation mode of the drone.

[0132] The beneficial effects of the above technical solution are: generating real-time drone mission planning based on the drone's location information combined with real-time changes in meteorological conditions, and visually displaying and updating it in real time through a visual map, obtaining dynamic flight situation monitoring of the drone. The visual map can clearly display the terrain, landform and other information of the mission area, which is convenient for drone pilots to perform tasks and improve mission efficiency. At the same time, the operating status of the drone can be grasped in real time, and the scheduling and management of the drone can be optimized.

[0133] Example 6:

[0134] Based on Example 5, the trigger module of this embodiment of the present invention includes:

[0135] The fifth acquisition unit: obtains the performance data and limitation parameters of the drone according to the drone documentation;

[0136] The sixth acquisition unit is configured to acquire the real-time environmental condition parameters of the UAV based on multiple devices of the UAV;

[0137] A third determining unit: establishing a model based on a machine learning algorithm according to the performance parameters of the UAV and the real-time environmental condition parameters to determine the optimal flight parameters of the UAV;

[0138] Setting unit: setting a safety threshold for UAV flight based on the optimal flight parameters of the UAV and dynamic flight situation monitoring of the UAV;

[0139] A judgment unit: judging the real-time flight status and real-time flight parameters of the UAV according to the safety threshold of the UAV flight;

[0140] Extraction unit: If the judgment result exceeds the safety threshold, an alarm is triggered, key feature information in the alarm information is extracted, and visual management of the UAV route is achieved based on the key feature information.

[0141] In this embodiment, the performance data includes: maximum load capacity, maximum speed, and battery power.

[0142] In this embodiment, the limiting parameters include: a maximum allowable tilt angle and a maximum tolerable wind speed.

[0143] In this embodiment, the real-time environmental condition parameters include: current temperature, air pressure, wind direction, and wind speed.

[0144] In this embodiment, the optimal flight parameters refer to a set of flight settings that can maximize the performance and safety of the drone, including: the aircraft's flight mode, speed controller settings, attitude control settings, battery charging settings, and GPS settings.

[0145] In this embodiment, in the alarm information, key characteristic information includes: location information, time information, and description information.

[0146] The beneficial effects of the above technical solution are: determining the optimal flight parameters of the UAV based on the performance parameters of the UAV and the real-time environmental condition parameters, setting the safety threshold of the UAV and judging the real-time flight status and real-time flight parameters of the UAV, triggering an alarm based on the judgment results, and being able to promptly determine abnormal flight status and abnormal flight parameters during the flight process, issuing an alarm when abnormal behavior is discovered, and improving flight safety.

[0147] Example 7:

[0148] Based on Example 6, the embodiment of the present invention further includes:

[0149] Query module: determines the corresponding operation interface according to the user's needs and usage habits, and conducts multi-angle and multi-form queries based on the operation interface;

[0150] Permission authentication module: obtains the identity information of the querying user, performs permission authentication based on the identity information, and determines whether the user is an internal user of the data access object based on the authentication result. If not, obtains the external user access security policy in the data access scenario;

[0151] The second determination module: determines the data description parameters within the user's access permission range according to the external user access security policy;

[0152] Retrieval module: retrieves relevant permission data from the data repository of the data access object according to the data description parameters and the user's type of data to be retrieved.

[0153] In this embodiment, multi-angle means that it can be filtered by task name, type, time range, etc., and it can also support functions such as fuzzy query and advanced search. The query results can be displayed in the form of lists, tables, etc., and can be sorted and filtered.

[0154] In this embodiment, multiple formats refer to input via voice or uploading pictures.

[0155] In this embodiment, user identity information refers to personal sensitive information such as the user's name, ID number, mobile phone number, etc.

[0156] In this embodiment, permission authentication is a security mechanism that verifies the identity of a user and authorizes the user to access specific resources or perform specific operations.

[0157] In this embodiment, the internal user of the data access object refers to a person or organization that uses the software system to perform operations, and may be an employee, partner, or other third-party user of the company.

[0158] In this embodiment, the external user access security policy is a security measure taken to prevent access by unauthorized users or unauthorized devices, applications, and data.

[0159] In this embodiment, the data description parameter refers to a parameter representing the characteristics of the input data, such as data quantity, data size, and data format.

[0160] The beneficial effects of the above technical solution are: determining the corresponding operation interface according to the user's habits, authenticating the user, determining the data description parameters within the user's access permission range to retrieve relevant permission data, which can ensure the security and privacy of the data and improve the reliability of the UAV route visualization management system.

[0161] Example 8:

[0162] Based on Example 7, the setting unit in this embodiment of the present invention includes:

[0163] The first determination subunit: obtains the flight area review parameters of the UAV, and determines the macro flight situation index set and the micro flight situation index set of the UAV based on the review parameters;

[0164] Generating subunit: generating a flight point sequence of the UAV based on the UAV's optimal flight parameters and the UAV's macroscopic flight situation index set and microscopic flight situation index set;

[0165] The first acquisition subunit: maps the drone's flight point sequence and obtains the drone's flight status heat map;

[0166] The second determination subunit: determines the flight altitude and flight attitude information of the UAV according to the flight status thermal map;

[0167] The third determining subunit determines the flight threat source information and flight power source information of the UAV based on the flight altitude information and flight attitude information combined with the environmental parameter information of the flight area;

[0168] Establish subunits: establish threat situation quantification matrix and power situation quantification matrix based on flight threat source information and flight power source information;

[0169] The fourth determination subunit: determines multiple flight decision indicators and specific indicator reference values ​​through the threat situation quantification matrix and the power situation quantification matrix;

[0170] Setting subunit: Set the safety threshold of UAV flight based on flight decision indicators and specific indicator reference values.

[0171] In this embodiment, the drone flight area review parameters refer to a series of parameters for identifying and planning the airspace in which the drone can fly in the drone system, including: no-fly zones, airspace restrictions, weather conditions, and obstacle detection.

[0172] In this embodiment, the macroscopic flight situation indicator set of the UAV refers to a set of indicators for monitoring and evaluating the entire flight process of the UAV, such as: position, heading angle and roll angle, attitude, and remote control signal strength.

[0173] In this embodiment, the microscopic flight situation indicator set of the drone refers to a set of indicators for monitoring and evaluating small changes in the drone's flight process, such as acceleration, yaw rate, pitch rate, and roll rate.

[0174] In this embodiment, the flight point sequence of the UAV refers to a sequence of multiple control points determined when planning the flight path of the UAV, including: a take-off point, a hovering point, a landing point, and a return point.

[0175] In this embodiment, the heat map of the flight status of the drone is a visualization tool for visualizing the movement status of the drone at different times, locations and altitudes. The heat map presents the location information of the drone in a color-coded manner, where the depth of the color represents the number of drones in the area.

[0176] In this embodiment, the drone flight threat source information refers to various factors and sources that may pose a threat to the drone flight, such as:

[0177] Human interference: For example, an illegal intruder attempts to damage or interfere with the operation of a drone, or a drone operator accidentally presses the wrong button, causing loss of control.

[0178] Natural environment: such as bad weather, natural disasters (such as floods, earthquakes, typhoons, etc.) and terrain obstacles.

[0179] Electronic jamming: such as electromagnetic pulses, laser jamming, and communications jamming.

[0180] Other aircraft and aerial vehicles: such as military aircraft, private jets and other drones.

[0181] In this embodiment, the flight power source information of the UAV refers to the parameters of the equipment and technology that can provide power and control for the UAV, such as battery capacity and voltage, motor power, sensor data, GPS and inertial navigation system.

[0182] In this embodiment, the threat situation quantification matrix is ​​a method for evaluating the threat level in different scenarios. Various threat factors are scored and classified to determine their impact and priority on the drone. The matrix uses a table consisting of multiple threat factors to represent the threat level and assigns a numerical score to each factor. Each factor may be scored based on factors such as its severity in the scenario, the possibility of occurrence, and the impact on the drone, ultimately forming a threat situation quantification matrix.

[0183] In this embodiment, the power status quantification matrix is ​​a method for evaluating and comparing the power status of a UAV. It is usually a table consisting of multiple power status factors. Each factor is scored based on its importance, frequency of occurrence, and impact on the system, and is classified as good, medium, or poor. By scoring and classifying these factors, a power status quantification matrix can be obtained.

[0184] In this embodiment, the multiple flight decision indicators are multiple parameters used to evaluate and determine whether the UAV can safely take off and perform the mission, such as: location information, route design, and remote control.

[0185] The beneficial effects of the above technical solution are: by determining multiple flight decision indicators and specific indicator reference values ​​through the threat situation quantification matrix and the power situation quantification matrix, the safety threshold of the drone flight is set, which can ensure that the drone can comply with safety standards and regulations in various situations, avoid unexpected situations when the drone is performing tasks, and at the same time reduce human errors, ensure that the drone can remain stable and safe in all situations, and further improve the flight safety of the drone.

[0186] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A UAV route visualization management system, characterized by: include: Collection module: obtains the model of the target drone, determines the corresponding sensor based on the model, and collects the drone's flight data in real time based on the sensor; A first determination module processes the flight data using data fusion technology, and determines the optimal route of the UAV based on the processing results and a path planning algorithm; Dynamic adjustment module: obtains the real-time surrounding environment parameters of the target UAV and dynamically adjusts the optimal route of the UAV according to the real-time surrounding environment parameters; Display module: Based on GIS technology, the adjusted flight trajectory of the UAV and weather conditions are visualized and updated in real time to obtain dynamic flight situation monitoring of the UAV; Trigger module: Sets safety thresholds based on the UAV's performance and real-time environmental conditions based on the UAV's dynamic flight situation monitoring, triggers alarms based on the safety thresholds, and implements visual management of the UAV's route based on the alarm information; Setting unit: setting a safety threshold for UAV flight based on the optimal flight parameters of the UAV and dynamic flight situation monitoring of the UAV; Setting unit, including: The first determination subunit: obtains the flight area review parameters of the UAV, and determines the macro flight situation index set and the micro flight situation index set of the UAV based on the review parameters; Generating subunit: generating a flight point sequence of the UAV based on the UAV's optimal flight parameters and the UAV's macroscopic flight situation index set and microscopic flight situation index set; The first acquisition subunit: maps the drone's flight point sequence and obtains the drone's flight status heat map; The second determination subunit: determines the flight altitude and flight attitude information of the UAV according to the flight status thermal map; The third determining subunit determines the flight threat source information and flight power source information of the UAV based on the flight altitude information and flight attitude information combined with the environmental parameter information of the flight area; Establish subunits: establish threat situation quantification matrix and power situation quantification matrix based on flight threat source information and flight power source information; The fourth determination subunit: determines multiple flight decision indicators and specific indicator reference values ​​through the threat situation quantification matrix and the power situation quantification matrix; Setting subunit: Set the safety threshold of UAV flight based on flight decision indicators and specific indicator reference values.

2. The UAV route visualization management system according to claim 1 is characterized in that: Collection modules, including: A first determining unit is configured to obtain structural and appearance characteristics of a target UAV and determine the model of the target UAV based on the structural and appearance characteristics. A first acquiring unit: determining sensor specifications and compatibility requirements according to the model, and acquiring a corresponding sensor based on the sensor specifications and compatibility requirements; Configuration unit: Configure the sensor interface and communication protocol according to the product manual to obtain real-time flight data of the drone.

3. The UAV route visualization management system according to claim 1, characterized in that: The first determination module includes: Data fusion unit: fuses the flight data acquired by different sensors based on fusion algorithms; The first generation unit generates the flight path points and flight path of the UAV based on the fused data and pre-defined navigation rules; Comparison unit: uses the fused data to determine the distance between each adjacent path point, compares the length of each path based on the distance between each path point, and obtains the comparison result; The second acquisition unit: obtains the optimal route of the UAV based on the comparison result and the obstacle avoidance algorithm to minimize the path length and the time to avoid obstacles.

4. The UAV route visualization management system according to claim 1, characterized in that: Dynamic adjustment module, including: The third acquisition unit: acquires the real-time surrounding environment parameters of the target UAV based on sensors related to environmental perception; Identification and filtering unit: identifies and filters the surrounding environment parameters to obtain key environmental parameters; Adjustment unit: adjusts the flight mode and safety parameters of the drone based on the heuristic algorithm according to the pre-set destination information and current environmental conditions; Dynamic adjustment unit: dynamically adjusts the optimal route of the drone based on the adjustment results.

5. The UAV route visualization management system according to claim 1, characterized in that: Display module, including: A second determining unit is configured to obtain an adjusted flight trajectory of the UAV based on the fuel consumption and time delay of the UAV, and determine the position information of the UAV based on the flight trajectory; The second generation unit is configured to obtain real-time changes in meteorological conditions and generate a real-time UAV mission plan based on the UAV's location information; Display unit: Generate a visual map based on GIS technology according to the UAV mission plan for visual display and update in real time; The fourth acquisition unit generates a real-time task scheduling suggestion according to the update result, and obtains the dynamic flight situation monitoring of the UAV according to the task scheduling suggestion.

6. The UAV route visualization management system according to claim 1, characterized in that: Trigger module, including: The fifth acquisition unit: obtains the performance data and limitation parameters of the drone according to the drone documentation; The sixth acquisition unit is configured to acquire the real-time environmental condition parameters of the UAV based on multiple devices of the UAV; A third determining unit: establishing a model based on a machine learning algorithm according to the performance parameters of the UAV and the real-time environmental condition parameters to determine the optimal flight parameters of the UAV; A judgment unit: judging the real-time flight status and real-time flight parameters of the UAV according to the safety threshold of the UAV flight; Extraction unit: If the judgment result exceeds the safety threshold, an alarm is triggered, key feature information in the alarm information is extracted, and visual management of the UAV route is achieved based on the key feature information.

7. The UAV route visualization management system according to claim 1, characterized in that: Also includes: Query module: determines the corresponding operation interface according to the user's needs and usage habits, and conducts multi-angle and multi-form queries based on the operation interface; Permission authentication module: obtains the identity information of the querying user, performs permission authentication based on the identity information, and determines whether the user is an internal user of the data access object based on the authentication result. If not, obtains the external user access security policy in the data access scenario; The second determination module: determines the data description parameters within the user's access permission range according to the external user access security policy; Retrieval module: retrieves relevant permission data from the data repository of the data access object according to the data description parameters and the user's type of data to be retrieved.

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