Intelligent flight control system of unmanned aerial vehicle

Abstract

The invention relates to the technical field of unmanned aerial vehicle flight control, and discloses an unmanned aerial vehicle intelligent flight control system, which comprises an emotion recognition sensor group used for collecting physiological signals, field audios and videos of field crowds in real time and obtaining field conditions; the positioning system obtains position coordinates of the unmanned aerial vehicle and feeds back the position coordinates to the central processing unit for flight path analysis; the central processing unit is used for performing signal processing on the field condition and the position coordinates and outputting a flight task; the wireless communication module is used for data transmission among all parts of the system; the unmanned aerial vehicle cluster executes the flight task output by the central processing unit; the emotional state of the audience is collected in real time through the emotional recognition sensor group, the central processing unit and the emotional recognition sensor group work cooperatively, the system can dynamically adjust the flight formation and the light effect of the unmanned aerial vehicle according to the field condition, resonance is generated with the emotion of the audience, and the interactivity and the sense of participation of activities are improved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) flight control technology, and more specifically, to an intelligent flight control system for UAVs. Background Technology

[0002] With the rapid development of drone technology, drones are increasingly being used in commerce, entertainment, security, and other fields. Especially in commercial marketing and event planning, drone formation flight displays have become an important means of attracting audience attention due to their visual impact and innovation.

[0003] However, existing drone swarm flight systems rely primarily on pre-set flight paths and lighting effects, lacking the ability to respond in real-time to the on-site environment and the emotional state of the audience. This inflexible control method makes it difficult to achieve interaction and emotional resonance with the audience in dynamically changing event scenarios. Summary of the Invention

[0004] This invention provides an intelligent flight control system for unmanned aerial vehicles (UAVs), which solves the technical problem that UAV formation flight systems lack real-time response capabilities to the on-site environment and the emotional state of the audience.

[0005] This invention provides an intelligent flight control system for unmanned aerial vehicles (UAVs), comprising: Emotion recognition sensor array: Real-time acquisition of physiological signals, audio and video from the crowd at the scene to obtain information about the situation. Positioning system: Acquires the drone's location coordinates and feeds them back to the central processing unit for flight path analysis; Central Processing Unit (CPU): Processes signals based on the situation and location coordinates on-site, and outputs the flight mission. Wireless communication module: used for data transmission between different parts of the system; Drone swarm: Executes flight missions output by the central processing unit.

[0006] Furthermore, the emotion recognition sensor group includes multiple distributed camera and microphone arrays for collecting facial expressions, voice tone and body movement signals, and transmitting the signals to the central processing unit through a communication module.

[0007] Furthermore, the central processing unit includes a GPU server that executes emotion recognition algorithms and formation planning algorithms. The central processing unit generates control commands for the drone's flight path and lighting effects by analyzing emotional state and activity rhythm information.

[0008] Furthermore, the drone swarm includes multiple small quadcopter drones equipped with LED lighting systems. Each drone performs trajectory planning and attitude control according to received instructions. The drone's flight control system adjusts the flight path and lighting effects according to control instructions generated by the central processor.

[0009] Furthermore, the positioning system employs UWB technology to acquire the UAV's location coordinates in real time and feeds the location data back to the central processing unit to dynamically adjust the flight path.

[0010] A method for intelligent flight control of unmanned aerial vehicles (UAVs), using the aforementioned intelligent flight control system for UAVs, includes the following steps: Signal acquisition: Emotional intensity information and activity rhythm information are obtained by collecting physiological signals of the crowd, audio and video data of the scene through the emotion recognition sensor group, and the activity theme information is obtained by inputting text materials provided by the event organizer; Signal processing: Processing the acquired signals; Decision control: Based on the numerical data after signal processing, plan the UAV flight formation and lighting effects settings; Execution feedback: The target location coordinates and lighting parameters are sent to the drone, which then performs flight and lighting display operations according to the instructions.

[0011] Furthermore, the data processing includes sentiment intensity analysis, activity theme analysis, and activity rhythm analysis, specifically including the following steps: recording the acquired sentiment intensity information as a sentiment intensity set, calculating the overall sentiment intensity index, and the calculation formula is: ; Number of people at the scene; The emotional intensity value of the j-th person; Emotional popularity index; The Word2Vec model was used to perform semantic analysis on the event topic information to obtain the topic key element vector; The acquired activity rhythm information is recorded as a rhythm time series. The rhythm time series is analyzed using the ARMA(p,q) model to output the rhythm change trend.

[0012] Furthermore, the decision control specifically includes the following steps: Flight Formation Planning: Based on the emotional heat index, the key element vector of the theme, and the rhythm change trend, the formation planning algorithm is used to determine the UAV flight formation. Let the number of UAVs be N, and the position coordinates of each UAV be: ; The coordinates are determined by solving an optimization model, where the optimization model is: ; Cost function; Number of drones; Predict the time step; Overall emotional heat index; : Vector of key elements of the topic; : The rhythm value of k at the current moment; Lighting effects settings: Based on the emotional heat index, the vector of key theme elements, and the rhythm change trend, the light color, brightness, and flicker frequency are calculated. The light color is determined through a functional relationship. The calculation involves first selecting a base color tone based on the key element vector of the theme, and then adjusting the color saturation and brightness according to the emotional heat index. The calculation formula is as follows: ; Hue value; : Saturation; Brightness; Overall emotional heat index; : Vector of key elements of the topic; : The rhythm value of k at the current moment; Light color; The brightness is related by a function. The brightness is adjusted based on the emotional heat index and rhythm value. The calculation formula is as follows: ; and These are the weighting coefficients. ; Base brightness value; Overall emotional heat index; : The rhythm value of k at the current moment; Brightness value; The flicker frequency is related to a function. The calculation is mainly determined by the rhythm value and the trend of rhythm change. The calculation formula is as follows: ; : Fundamental frequency; : Flashing frequency; and These are the weighting coefficients. ; The rate of change of rhythm value is calculated using the following formula: : The rhythm value of k at the current moment; : The rhythm value at time k-1.

[0013] Furthermore, the execution feedback specifically includes the following steps: The central processing unit transmits the target location coordinates and lighting parameters to each drone via the wireless communication module; The flight control system of each UAV performs trajectory planning and attitude control based on the received target position, and the LED lighting system adjusts the lighting effect according to the received parameters; The positioning system provides real-time feedback on the actual location of the drone, and the system dynamically adjusts control commands based on the feedback information.

[0014] A computer-readable storage medium for storing computer-readable instructions that, when read by a computer, enable the execution of an intelligent flight control method for an unmanned aerial vehicle (UAV) as described above.

[0015] The beneficial effects of this invention are as follows: by collecting the emotional state of the audience in real time through the emotion recognition sensor group, the central processing unit and the emotion recognition sensor group work together, and the system can dynamically adjust the flight formation and lighting effects of the drones according to the on-site situation, resonate with the emotions of the audience, and enhance the interactivity and participation of the event; the automated control of the system reduces the reliance on manual operation, reduces labor costs, and improves the organization efficiency of the event. Attached Figure Description

[0016] Figure 1 This is a system architecture diagram of the unmanned aerial vehicle (UAV) intelligent flight control system in this invention; Figure 2 This is a flowchart of the intelligent flight control method for unmanned aerial vehicles in this invention. Detailed Implementation

[0017] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples. Example 1

[0018] This invention discloses an intelligent flight control system for unmanned aerial vehicles (UAVs), such as... Figure 1 As shown, it includes: Emotion recognition sensor array: Real-time acquisition of physiological signals, audio and video from the crowd at the scene to obtain information about the situation. Positioning system: Acquires the drone's location coordinates and feeds them back to the central processing unit for flight path analysis; Central Processing Unit (CPU): Processes signals based on the situation and location coordinates on-site, and outputs the flight mission. Wireless communication module: used for data transmission between different parts of the system; Drone swarm: Executes flight missions output by the central processing unit.

[0019] Furthermore, the emotion recognition sensor group includes multiple distributed camera and microphone arrays for collecting facial expressions, voice tone and body movement signals, and transmitting the signals to the central processing unit through a communication module.

[0020] Furthermore, the central processing unit includes a GPU server that executes emotion recognition algorithms and formation planning algorithms. The central processing unit generates control commands for the drone's flight path and lighting effects by analyzing emotional state and activity rhythm information.

[0021] Furthermore, the drone swarm includes multiple small quadcopter drones equipped with LED lighting systems. Each drone performs trajectory planning and attitude control according to received instructions. The drone's flight control system adjusts the flight path and lighting effects according to control instructions generated by the central processor.

[0022] Furthermore, the positioning system employs UWB (Ultra Wide Band) technology to acquire the UAV's location coordinates in real time and feed the location data back to the central processing unit to dynamically adjust the flight path.

[0023] At least one embodiment of the present invention discloses an intelligent flight control method for unmanned aerial vehicles (UAVs), such as... Figure 2 As shown, it includes the following steps: Step 1, Signal Acquisition: Acquire information on emotional intensity, activity rhythm, and activity theme; Step 1-1, Obtaining Emotional Heat Information, specifically includes the following steps: Step 1-1-1: The emotion recognition sensor group collects physiological signals from the crowd in real time, including facial expressions, voice tone and body movements. Step 1-1-2: The collected signal is transmitted to the central processing unit via the wireless communication module; Step 1-1-3 yields the emotional intensity set. The formula for calculating the emotional intensity set is: ; , , These are the emotional intensity values ​​for the first, second, and nth people, respectively. Let represent the emotional intensity value of the j-th person, ranging from [0,1]. A larger value indicates a higher emotional intensity. The formula for calculating the emotional intensity value is: ; Personnel ID , This represents the total number of people present. , , These are the emotional probability distributions of the j-th person's facial expressions, tone of voice, and body movements, respectively. They are all 7-dimensional vectors, with each dimension corresponding to the probability value of a basic emotion (happiness, sadness, anger, disgust, fear, surprise, and neutrality). , , These are the first, second, and third weighting coefficients, and their specific values ​​can be adjusted according to the actual application scenario. Furthermore, the emotional intensity dataset requires obtaining the emotional probability distribution of facial expressions, tone of voice, and body language of the people present, specifically including: Step 1-1-3-1, Facial Expression Analysis: Convolutional Neural Networks (CNNs) are used to extract features from facial images to obtain expression feature vectors. The probability distribution of facial expressions is then calculated using the following formula: ; : The facial expression feature vector of the j-th person, n is the total number of people present; : The feature vector of the i-th basic expression It corresponds to 7 basic emotions (happiness, sadness, anger, disgust, fear, surprise, and neutrality). : Natural exponential function, with the natural constant e as the base; Step 1-1-3-2, Speech and Intonation Analysis: Acoustic features are extracted by performing a Short-Time Fourier Transform (STFT) on the audio signal, and temporal features are processed using a Long Short-Time Memory (LSTM) network. The emotional probability distribution of speech intonation is calculated using the following formula: ; The speech feature vector of the j-th person. n is the total number of people present; : Feature vector of the i-th basic language emotion It corresponds to 7 basic emotions (happiness, sadness, anger, disgust, fear, surprise, and neutrality). Step 1-1-3-3, Body Movement Analysis: The OpenPose human pose estimation algorithm was used to calculate the pose features of the people on site. A two-stream spatiotemporal graph convolutional network (ST-GCN) was used to process the pose features and map the emotional probability distribution of body movements. The formula is as follows: ; : The action feature vector of the j-th person n is the total number of people present; : The feature vector of the i-th basic action emotion It corresponds to 7 basic emotions (happiness, sadness, anger, disgust, fear, surprise, and neutrality). Steps 1-2, Obtaining Event Theme Information: The system reads the text materials provided by the event organizer and parses them using natural language processing technology to obtain the theme information.

[0024] Steps 1-3, Acquisition of Activity Rhythm Information: The system collects on-site audio and video data at m consecutive time points in real time and analyzes the data to obtain the rhythm time series. ; in , , These represent the rhythmic feature values ​​at time points 1, 2, and m, respectively. Represents a rhythmic time series.

[0025] Step 2, Signal Processing: Process the acquired signal; Step 2-1, Sentiment Heat Analysis, specifically includes the following steps: Step 2-1-1: Input the emotional heat set; Step 2-1-2, calculate the overall emotional heat index: ; Number of people at the scene The emotional intensity value of the j-th person; Emotional popularity index; Step 2-1-3: Output the overall emotional heat index; Step 2-2, Activity Theme Analysis, specifically includes the following steps: Step 2-2-1, Enter the event theme information (in text format); Step 2-2-2: Perform semantic analysis using the Word2Vec model to obtain the topic key element vector: ; The word frequency of the i-th word; : The word vector (d-dimensional vector) of the i-th word; : The final key element vector of the topic (also a d-dimensional vector); i: Current word number; n: Total number of words in the text; Step 2-2-3, output the topic key element vector (d-dimensional vector); Steps 2-3, Activity Rhythm Analysis, specifically include the following steps: Step 2-3-1, Input rhythm time series; Step 2-3-2: Use the ARMA(p,q) model to analyze the time series characteristics and obtain the rhythm value at the current moment. The specific mathematical expression is as follows: ; : The rhythm value of k at the current moment; The order of the autoregressive term; The order of the moving average term; : constant term; Autoregressive coefficient ; Moving average coefficient ; The random error term follows a normal distribution. : The rhythm value of the i-th period lag; Random error with a lag of j periods; Step 2-3-3: Output the rhythm change trend, which includes: rhythm value sequence, trend type, and rate of change. The rhythm value sequence is... ; in, , , These are the rhythm values ​​at the current time k, the (k+1)th time, and the (k+h)th time, respectively; Trend types include monotonically increasing, monotonically decreasing, or fluctuating, with the rate of change being the rate of change of the rhythm value.

[0026] Step 3, Decision Control: Based on the numerical data after signal processing, plan the UAV flight formation and lighting effects settings; Step 3-1, Flight Formation Planning: Step 3-1-1: Input the emotional heat index, the key element vector of the theme, and the rhythm change trend; Step 3-1-2, Processing Input Data: Use a formation planning algorithm to determine the UAV flight formation. Let the number of UAVs be N, and the coordinates of each UAV's position be: ; The coordinates are determined by solving an optimization model, where the optimization model is: ; : The three-dimensional coordinates of the i-th UAV Total number of drones Predict the time step. The tempo value of k at the current moment. Overall emotional heat index : Vector of key elements of the topic; The cost function is defined as follows: ; The distance between the drone's location and other locations related to the topic; : The rhythm value of k at the current moment; The desired rhythm value determined based on the rhythm change trend (trend type and rate of change); : Reference value for emotional intensity; , , These are the weighting coefficients; Step 3-1-3: Output the target position coordinates of N drones. ; Step 3-2, Lighting Effects Settings: Step 3-2-1: Input the sentiment heat index, the key element vector of the topic, and the rhythm change trend (including the predicted rhythm value sequence, trend type, and rate of change). Step 3-2-2: Calculate the lighting parameters, including light color, brightness, and flicker frequency. Light color is determined by a functional relationship The calculation involves first selecting a base color tone based on the key element vectors of the theme, and then adjusting the color saturation and brightness according to the emotional intensity index. For example, when the emotional intensity index is high... The output is a vibrant color; when When the pace is fast, The output is a variety of color combinations, and the specific calculation formula is as follows: ; Emotional popularity index; : Vector of key elements of the topic; : The rhythm value of k at the current moment; Light color; Hue value, ranging from [0°, 360°]; : Saturation, with a value range of [0,1], where: ; Minimum value function : Saturation adjustment coefficient, with a value of a positive real number; : Brightness, with a value range of [0,1], where: ; : Brightness adjustment coefficient, with a value of a positive real number; Brightness through functional relationship The calculation is performed to adjust the brightness based on the emotional intensity index and the rhythm value. For example, when the emotional intensity index is high and the rhythm is fast, a higher brightness value is output. The specific calculation formula is as follows: ; Based on the basic brightness value, The coefficient representing the influence of emotional intensity. The rhythm influence coefficient. The output brightness value ranges from [0,1]. Emotional heat index : The rhythm value of k at the current moment; The flicker frequency is related to a function. The calculation is mainly determined by the rhythm value and the trend of rhythm changes. For example, a faster rhythm results in a higher flashing frequency. The specific calculation formula is as follows: ; Base frequency; , This is the adjustment coefficient; The blinking frequency; The rate of change of rhythm value is calculated using the following formula: ; This represents the rhythm value of k at the current moment.

[0027] Step 3-2-3: Output the lighting parameters.

[0028] Step 4, Execution Feedback: Send the target location coordinates and lighting parameters to the drone, and the drone will execute flight and lighting display operations according to the instructions; Step 4-1, Drone control execution: Step 4-1-1: The central processing unit sends the target location coordinates and lighting parameters to each drone via the wireless communication module; Step 4-1-2: The flight control system of each UAV performs trajectory planning and attitude control based on the received target position; Step 4-1-3: The LED lighting system adjusts the lighting effect according to the received parameters; Step 4-2, Status Feedback: Step 4-2-1: The positioning system provides real-time feedback on the actual location of the drone; Step 4-2-2: The system dynamically adjusts the control commands based on the feedback information.

[0029] A computer-readable storage medium for storing computer-readable instructions that, when read by a computer, enable the execution of an intelligent flight control method for an unmanned aerial vehicle (UAV) as described above. Example 2

[0030] This invention provides an example of an intelligent flight control system for a drone in a brand experience store setting, comprising: The emotion recognition sensor group is changed to a small and concealed type, integrated at the entrance of the exhibition area; New exhibition area division sensors are added to identify the exhibition area where the customer is located; The number of the drone cluster is reduced to M (M < N), and smaller indoor drones are used to adapt to the space limitation of the exhibition hall; The positioning system adds infrared sensors for more precise obstacle avoidance.

[0031] Furthermore, emotion heat information is added to information acquisition, and the content of the activity theme information is adjusted; Specifically, the residence time of the customer in different exhibition areas is obtained through the exhibition area division sensor and the behavior analysis algorithm: ; Among them, 、 、 respectively represent the residence time of the customer in the 1st, 2nd, and kth exhibition areas, is the set of residence times in different exhibition areas; The shopping intention degree of the customer: ; Among them, 、 、 respectively represent the shopping intention degrees of the customer in the 1st, 2nd, and kth exhibition areas, is the set of shopping intention degrees in different exhibition areas.

[0032] Specifically, the acquisition of the activity theme information is changed to read the brand display content and the commodity category information, and the brand theme key element vector is obtained based on the pre-trained language model of the Transformer architecture ; Furthermore, a weight factor is added when processing the emotion heat information, and the other steps are the same as those in Embodiment 1 and will not be elaborated; Specifically, a weight factor is added to the calculation of the emotion heat index: ; : Residence time influence factor, : Influence factor of shopping intention, : Emotion heat index in the brand experience store, : Emotion heat value of the jth customer in the brand experience store, , n is the total number of people on site; Among them, the calculation formula of the residence time influence factor is: ; : Reference residence time, : The time a customer spends in the kth exhibition area Hyperbolic tangent function; Formula for calculating the influencing factors of shopping intention: ; in, To provide a reference for shopping intentions, The customer's shopping intention in the kth exhibition area.

[0033] Furthermore, considering the exhibition area layout constraints, the flight formation planning includes M drones, with each drone's position coordinates as follows: The coordinates are determined by solving the optimization model: ; in, It is a cost function that comprehensively considers emotional appeal, the pace of the experiential store, and the brand theme, similar to the original plan. These are weighting coefficients. It is a constraint function for a confined indoor space. Indicates at a point in time The rhythmic characteristic value.

[0034] Furthermore, the lighting effect setting steps are the same as in Example 1, wherein the adjustment coefficient is set according to brand preference. Example 3

[0035] This invention also provides an example of an implementation, a drone intelligent flight control system for a themed restaurant setting, comprising: The emotion recognition sensor group is equipped with a thermal imaging camera to monitor the amount of food left over during the meal. A new table positioning sensor has been added to accurately locate the position of each seat. The drone features a waterproof and oil-resistant outer shell, meeting the needs of the catering industry. Add odor sensors to monitor the restaurant atmosphere.

[0036] Furthermore, information acquisition should incorporate emotionally relevant information and adjust the content of event-themed information. Specifically, meal progress information is obtained through miniature emotion recognition sensors, thermal imaging cameras, and odor sensors: ; in, , , These represent the customer's progress at the 1st, 2nd, and kth dining stages, respectively. This represents a set of dining progress information; Table atmosphere index: ; in, , , These represent the table atmosphere index for customers at the 1st, 2nd, and kth dining stages, respectively. This represents a set of table atmosphere indices.

[0037] Specifically, the acquisition of event theme information has been changed to reading restaurant theme and menu feature information, and using named entity recognition technology to parse and obtain the key element vector of the restaurant theme. .

[0038] Furthermore, meal progress information should be considered when processing emotional intensity information; Specifically, the calculation of the emotional intensity index takes into account the dining stage: ; in, Factors affecting meal progress Factors influencing the dining atmosphere Emotional warmth index within the restaurant The emotional intensity value of the j-th customer in the restaurant. n is the total number of people present; Formula for calculating the influencing factor of meal progress: ; in, For reference of the meal progress, For the customer's dining progress in the kth exhibition area, It is the hyperbolic tangent function; Formula for calculating factors influencing dining atmosphere: ; in, To reference the dining atmosphere index, The table atmosphere index for customers in the kth exhibition area.

[0039] Furthermore, the flight formation was planned to avoid the area above the dining area, and K drones were deployed, with the coordinates of each drone being [missing information]. The coordinates are determined by solving the optimization model: ; in, It is a cost function that comprehensively considers emotional appeal, restaurant rhythm, and restaurant theme, similar to the original solution. These are weighting coefficients. It is a constraint function specific to the restaurant space. Indicates at a point in time The rhythmic characteristic value.

[0040] Furthermore, the lighting effect setting steps are the same as in Example 1, wherein the adjustment coefficient is set to match the restaurant theme and dining atmosphere. Example 4

[0041] This invention also provides an example of an implementation, a drone intelligent flight control system for a cultural and artistic exhibition setting, comprising: The emotion recognition sensor array uses an ultra-high-definition camera to capture subtle changes in the audience's facial expressions; New exhibit positioning sensors have been added to monitor the location of important cultural relics in real time. The drones are ultra-quiet models to minimize interference with the exhibition environment; Add an ambient light sensor to complement the exhibition lighting system.

[0042] Furthermore, information acquisition should incorporate emotionally relevant information and adjust the content of event-themed information. Specifically, visitor route information is obtained through emotion recognition cameras and exhibit positioning sensors: ; in, , , These represent the visitor's viewing routes in the 1st, 2nd, and kth exhibition areas, respectively. Information on the exhibition route; Depth of cultural understanding: ; in, , , This indicates the depth of a visitor's cultural understanding in the 1st, 2nd, and kth exhibition areas. For a deeper understanding of culture.

[0043] Specifically, the acquisition of thematic information has been changed to reading the exhibition theme and artifact information, and using knowledge graph reasoning technology to analyze and obtain the vector of key elements of the cultural theme. ; Furthermore, cultural factors should be considered when processing emotional intensity information in signal processing; Specifically, the calculation of the emotional heat index takes cultural factors into account: ; in and These are the influencing factors of exhibition viewing progress and cultural understanding. The emotional intensity index within the exhibition hall The emotional intensity value of the j-th customer in the exhibition hall. n is the total number of people present; Formula for calculating the influencing factors on exhibition visit progress: ; in, To provide a reference for the exhibition visit schedule, For the visitor's viewing progress in the kth exhibition area, It is the hyperbolic tangent function; Formula for calculating the influencing factors of cultural understanding: ; in, For reference, depth of cultural understanding To assess the depth of cultural understanding among visitors in the k-th exhibition area.

[0044] Furthermore, the flight formation planning must maintain a safe distance to avoid affecting cultural relics. The number of drones is L, and the position coordinates of each drone are as follows: The coordinates are determined by solving the following optimization model: ; in, It is a cost function that comprehensively considers emotional intensity, cultural rhythm, and cultural theme, similar to the original solution. These are weighting coefficients. It is a constraint function specific to the restaurant space. Indicates at a point in time The rhythmic characteristic value.

[0045] Furthermore, the lighting effect setting steps are the same as in Example 1, wherein the adjustment coefficient is set according to the required cultural relic protection requirements.

[0046] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. An intelligent flight control system for unmanned aerial vehicles (UAVs), characterized in that, include: Emotion recognition sensor array: Real-time acquisition of physiological signals, audio and video from the crowd at the scene to obtain information about the situation. Positioning system: Acquires the drone's location coordinates and feeds them back to the central processing unit for flight path analysis; Central Processing Unit (CPU): Processes signals based on the situation and location coordinates on-site, and outputs the flight mission. Wireless communication module: used for data transmission between different parts of the system; Drone swarm: Executes flight missions output by the central processing unit.

2. The intelligent flight control system for unmanned aerial vehicles according to claim 1, characterized in that, The emotion recognition sensor group includes multiple distributed camera and microphone arrays for collecting facial expressions, voice tone and body movement signals, and transmitting the signals to the central processing unit through a communication module.

3. The intelligent flight control system for unmanned aerial vehicles according to claim 1, characterized in that, The central processing unit includes a GPU server that executes emotion recognition algorithms and formation planning algorithms. The central processing unit generates control commands for the drone's flight path and lighting effects by analyzing emotional state and activity rhythm information.

4. The intelligent flight control system for unmanned aerial vehicles according to claim 1, characterized in that, The drone swarm includes multiple small quadcopter drones equipped with LED lighting systems. Each drone performs trajectory planning and attitude control according to received instructions. The drone's flight control system adjusts the flight path and lighting effects according to control instructions generated by the central processor.

5. The intelligent flight control system for unmanned aerial vehicles according to claim 1, characterized in that, The positioning system uses UWB technology to acquire the UAV's position coordinates in real time and feeds the position data back to the central processor to dynamically adjust the flight path.

6. A method for intelligent flight control of an unmanned aerial vehicle (UAV), using an intelligent flight control system for an UAV as described in any one of claims 1-5, characterized in that, Includes the following steps: Signal acquisition: Emotional intensity information and activity rhythm information are obtained by collecting physiological signals of the crowd, audio and video data of the scene through the emotion recognition sensor group, and the activity theme information is obtained by inputting text materials provided by the event organizer; Signal processing: Processing the acquired signals; Decision control: Based on the numerical data after signal processing, plan the UAV flight formation and lighting effects settings; Execution feedback: The target location coordinates and lighting parameters are sent to the drone, which then performs flight and lighting display operations according to the instructions.

7. The intelligent flight control method for unmanned aerial vehicles according to claim 6, characterized in that, The data processing includes sentiment intensity analysis, activity theme analysis, and activity rhythm analysis, specifically including the following steps: The acquired sentiment intensity information is recorded as a sentiment intensity set, and the overall sentiment intensity index is calculated using the following formula: ； Number of people at the scene; The emotional intensity value of the j-th person; Emotional popularity index; The Word2Vec model was used to perform semantic analysis on the event topic information to obtain the topic key element vector; The acquired activity rhythm information is recorded as a rhythm time series. The rhythm time series is analyzed using the ARMA(p,q) model to output the rhythm change trend.

8. The intelligent flight control method for unmanned aerial vehicles according to claim 6, characterized in that, The decision control specifically includes the following steps: Flight Formation Planning: Based on the emotional heat index, the key element vector of the theme, and the rhythm change trend, the formation planning algorithm is used to determine the UAV flight formation. Let the number of UAVs be N, and the position coordinates of each UAV be: ； The coordinates are determined by solving an optimization model, where the optimization model is: ； Cost function; Number of drones; Predict the time step; Overall emotional heat index; : Vector of key elements of the topic; : The rhythm value of k at the current moment; Lighting effects settings: Based on the emotional heat index, the vector of key theme elements, and the rhythm change trend, the light color, brightness, and flicker frequency are calculated. The light color is determined through a functional relationship. The calculation involves first selecting a base color tone based on the key element vector of the theme, and then adjusting the color saturation and brightness according to the emotional heat index. The calculation formula is as follows: ； Hue value; : Saturation; Brightness; Overall emotional heat index; : Vector of key elements of the topic; : The rhythm value of k at the current moment; Light color; The brightness is related by a function. The brightness is adjusted based on the emotional heat index and rhythm value. The calculation formula is as follows: ； and These are the weighting coefficients. ; Base brightness value; Overall emotional heat index; : The rhythm value of k at the current moment; Brightness value; The flicker frequency is related to a function. The calculation is mainly determined by the rhythm value and the trend of rhythm change. The calculation formula is as follows: ； : Fundamental frequency; : Flashing frequency; and These are the weighting coefficients. ; The rate of change of rhythm value is calculated using the following formula: : The rhythm value of k at the current moment; : The rhythm value at time k-1.

9. The intelligent flight control method for unmanned aerial vehicles according to claim 6, characterized in that, The execution feedback specifically includes the following steps: The central processing unit transmits the target location coordinates and lighting parameters to each drone via the wireless communication module; The flight control system of each UAV performs trajectory planning and attitude control based on the received target position, and the LED lighting system adjusts the lighting effect according to the received parameters; The positioning system provides real-time feedback on the actual location of the drone, and the system dynamically adjusts control commands based on the feedback information.

10. A computer-readable storage medium, characterized in that, It is used to store computer-readable instructions, which, when read by a computer, enable the execution of an intelligent flight control method for unmanned aerial vehicles as described in any one of claims 6-9.

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