Patrol monitoring method for security unmanned aerial vehicle

Abstract

The invention is suitable for the technical field of security and protection monitoring, and provides a security and protection unmanned aerial vehicle patrol monitoring method, which comprises the following steps: receiving patrol path information which comprises a plurality of unmanned aerial vehicle numbers; image information uploaded by the unmanned aerial vehicle is received, the image information is identified based on the security features, and when the security features exist in the image information, tracking shooting is carried out; receiving monitoring demand information uploaded by the user terminal, wherein the monitoring demand information comprises a demand place and monitoring target information; determining a distance value of each unmanned aerial vehicle relative to the demand place and a safety coefficient of the current monitoring place, and determining the number of the unmanned aerial vehicle going to the demand place according to the distance value and the safety coefficient; and when the unmanned aerial vehicle going to the demand place arrives, tracking shooting is carried out based on the monitored target information. Therefore, when a student or a teacher feels that a security event occurs in a certain place in the campus, the unmanned aerial vehicle can go to the campus for monitoring through the user terminal, the degree of freedom is extremely high, and the recognition and monitoring effects are good.

Description

Technical Field

[0001] The present invention relates to the technical field of security monitoring, and in particular to a security drone patrol monitoring method. Background Art

[0002] With the continuous expansion of campus scale and the increase in the number of teachers and students, campus safety has become a highly concerned issue in all sectors of society. Traditional campus security methods mainly rely on manual patrols, video surveillance, and access control systems, but these methods have many limitations. With the rapid development of drone technology, drones are currently used for security patrols. Drones fly according to set patrol routes and upload captured images in real time to facilitate timely detection of safety hazards. However, when students or teachers feel that a security incident may occur somewhere on campus, drones cannot appear in time for security monitoring. Therefore, it is necessary to provide a security drone patrol monitoring method to solve the above problems. Summary of the invention

[0003] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a security drone patrol monitoring method to solve the problems existing in the above-mentioned background technology.

[0004] The present invention is implemented as follows: a security drone patrol monitoring method, the method comprising the following steps:

[0005] Receiving patrol path information, where the patrol path information includes a number of drone numbers, each drone number corresponding to a patrol path;

[0006] Receive image information uploaded by drones, identify the image information based on security features, and when security features are present in the image information, perform fixed-point tracking and shooting to generate early warning information;

[0007] Receiving monitoring demand information uploaded by a user terminal, wherein the monitoring demand information includes a demand location and monitoring target information;

[0008] Determine the distance value of each drone relative to the required location and the safety factor of the current monitoring location, and determine the drone number to go to the required location based on the distance value and safety factor;

[0009] When the drone arrives at the required location, it will track and shoot based on the monitoring target information.

[0010] As a further solution of the present invention: the step of identifying the image information based on the security feature and performing fixed-point tracking shooting when the image information contains the security feature specifically includes:

[0011] Extract feature information from image information based on computer vision algorithms;

[0012] Matching the feature information with the security feature, and when the matching degree is higher than the matching threshold, determining that the security feature exists in the image information, and marking the security feature as a target feature;

[0013] Start the tracking mode of the drone and perform fixed-point tracking and shooting of target features.

[0014] As a further solution of the present invention: the step of determining the distance value of each drone relative to the required location and the safety factor of the current monitoring location specifically includes:

[0015] Retrieve the coordinate information of each drone, and determine the distance value of each drone relative to the required location based on the coordinate information;

[0016] Retrieve the current shooting picture of each drone, determine whether the current shooting picture has security features, and when there are security features, determine the safety factor of the current monitoring location to be A, where A is a fixed value; otherwise, execute the next step;

[0017] Identify the crowding level of people in the current shooting picture and determine the safety factor based on the crowding level of people.

[0018] As a further solution of the present invention: the step of identifying the degree of crowding of people in the current shooting picture specifically includes:

[0019] Extracting crowd features in the current shot image through CNN, the crowd features include crowd density, distribution and movement direction;

[0020] Based on the extracted crowd features, a crowd density map is generated, and each pixel value in the crowd density map represents the crowd density at that location;

[0021] Integrate the crowd density map to obtain the total number of people in the current shooting picture and determine the degree of crowding.

[0022] As a further solution of the present invention: the safety factor is proportional to the degree of congestion, safety factor B=K*R, wherein K is a constant coefficient and R represents the degree of congestion.

[0023] As a further solution of the present invention: the step of tracking and shooting based on the monitoring target information specifically includes:

[0024] Determine the required detection area with the required location as the center and a fixed value R as the radius;

[0025] Collect the image of the required detection area, identify whether there is monitoring target information in the image, and if so, perform tracking shooting; otherwise, execute the next step;

[0026] The system sends monitoring target confirmation information to the user terminal and receives feedback information. When the feedback information is updated monitoring target information, the system performs tracking and shooting based on the latest monitoring target information. When the feedback information is confirmed, the system identifies whether the monitoring target information exists in the image again. If not, the system returns home.

[0027] As a further solution of the present invention: the monitoring demand information can also be accompanied by location information. When the location information is accompanied, the user terminal needs to enable location authorization; when the location information is accompanied, the demand location is determined to be the location information of the user terminal.

[0028] Another object of the present invention is to provide a security drone patrol monitoring system, the system comprising:

[0029] A patrol path information module, used to receive patrol path information, wherein the patrol path information includes a number of drone numbers, each drone number corresponds to a patrol path;

[0030] The security feature recognition module is used to receive the image information uploaded by the drone, identify the image information based on the security features, and perform fixed-point tracking and shooting to generate early warning information when the image information contains security features;

[0031] A monitoring demand information module is used to receive monitoring demand information uploaded by a user terminal, wherein the monitoring demand information includes a demand location and monitoring target information;

[0032] The drone dispatching module is used to determine the distance value of each drone relative to the required location and the safety factor of the current monitoring location, and determine the drone number to go to the required location based on the distance value and the safety factor;

[0033] The demand tracking and shooting module is used to track and shoot based on the monitoring target information when the drone arrives at the required location.

[0034] As a further solution of the present invention: the security feature recognition module includes:

[0035] A feature information extraction unit, used to extract feature information from image information based on a computer vision algorithm;

[0036] A feature information matching unit, used to match the feature information with the security feature, and when the matching degree is higher than a matching threshold, determine that the security feature exists in the image information, and mark the security feature as a target feature;

[0037] The target feature tracking unit is used to start the tracking mode of the drone and perform fixed-point tracking and shooting of the target features.

[0038] As a further solution of the present invention: the drone dispatching module includes:

[0039] A distance value determination unit, used to retrieve the coordinate information of each drone, and determine the distance value of each drone relative to the required location according to the coordinate information;

[0040] The first safety factor unit is used to retrieve the current shooting picture of each drone, determine whether the current shooting picture has security features, and when the security features exist, determine the safety factor of the current monitoring location to be A, where A is a fixed value; otherwise, execute the next step;

[0041] The second safety factor unit is used to identify the crowding degree of people in the current shooting picture and determine the safety factor according to the crowding degree of people.

[0042] As a further solution of the present invention: the second safety factor unit includes:

[0043] The crowd feature extraction subunit is used to extract crowd features in the current shot picture through CNN, where the crowd features include crowd density, distribution and movement direction;

[0044] A crowd density map subunit is used to generate a crowd density map based on the extracted crowd features, where each pixel value in the crowd density map represents the crowd density at that location;

[0045] The crowding degree subunit is used to integrate the crowd density map to obtain the total number of people in the current shooting picture and determine the crowding degree of the people.

[0046] As a further solution of the present invention: the demand tracking and shooting module includes:

[0047] The demand detection area unit is used to determine the demand detection area with the demand location as the center and a fixed value R as the radius;

[0048] The monitoring target determination unit is used to collect the image of the required detection area, identify whether there is monitoring target information in the image, and if so, perform tracking and shooting; otherwise, execute the next step;

[0049] The monitoring target confirmation unit is used to send monitoring target confirmation information to the user terminal and receive feedback information. When the feedback information is updated monitoring target information, tracking and shooting are performed based on the latest monitoring target information. When the feedback information is confirmed, it is re-identified whether there is monitoring target information in the image screen. If not, return.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention can identify image information based on security features. When security features exist in the image information, fixed-point tracking and shooting are performed, and the monitoring efficiency is high and the effect is good. The user terminal can also upload monitoring demand information, and determine the distance value of each drone relative to the required location and the safety factor of the current monitoring location. The number of the drone going to the required location is determined according to the distance value and the safety factor. When the drone going to the required location arrives, tracking and shooting are performed based on the monitoring target information. In this way, when students or teachers feel that a security incident will occur somewhere on campus, students or teachers can use the user terminal to order the drone to go there for monitoring, which has a high degree of freedom and is convenient and fast. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 The figure is a flow chart of a security drone patrol monitoring method.

[0053] Figure 2 The present invention is a flowchart for identifying image information in a security drone patrol monitoring method.

[0054] Figure 3 The present invention is a flowchart for determining the distance value and safety factor in a security drone patrol monitoring method.

[0055] Figure 4 The present invention is a flowchart for determining the crowding degree of personnel in a security drone patrol monitoring method.

[0056] Figure 5 The present invention is a flowchart of tracking and shooting based on monitoring target information in a security drone patrol monitoring method.

[0057] Figure 6 The figure is a structural diagram of a security drone patrol monitoring system. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0059] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0060] like Figure 1 As shown, an embodiment of the present invention provides a security drone patrol monitoring method, the method comprising the following steps:

[0061] S100, receiving patrol path information, where the patrol path information includes a number of drone numbers, each drone number corresponding to a patrol path;

[0062] S200, receiving image information uploaded by the drone, identifying the image information based on security features, and when the image information contains security features, performing fixed-point tracking and shooting to generate warning information;

[0063] S300, receiving monitoring demand information uploaded by a user terminal, wherein the monitoring demand information includes a demand location and monitoring target information;

[0064] S400, determining the distance value of each drone relative to the required location and the safety factor of the current monitoring location, and determining the number of the drone going to the required location according to the distance value and the safety factor;

[0065] S500, when the drone arrives at the required location, it will track and shoot based on the monitoring target information.

[0066] It should be noted that with the rapid development of drone technology, drones are currently used for security patrols. Drones will fly according to set patrol routes and upload captured images in real time to facilitate timely discovery of potential safety hazards. However, when students or teachers feel that a security incident may occur somewhere on campus, drones cannot appear in time for security monitoring. The embodiments of the present invention aim to solve the above problem.

[0067] In the embodiment of the present invention, it is first necessary to formulate patrol path information and upload it. The patrol path information includes several drone numbers, each drone number corresponds to a patrol path, and each patrol path will be sent to the controller of the corresponding drone, so that the drone flies according to the set route. During the flight patrol, the drone will upload image information in real time. The embodiment of the present invention will identify the image information based on security features. When there are security features in the image information, it means that there is a security risk. For example, the security features include flames, smoke, fighting, falling, etc. At this time, fixed-point tracking and shooting will be performed, and early warning information will be generated. The early warning information includes time, location and image, and the early warning information will be sent to the security personnel terminal of the campus. When students or teachers feel that a security incident may occur somewhere on campus, students or teachers can upload monitoring demand information through the user terminal. The monitoring demand information includes the required location and monitoring target information. Preferably, the monitoring demand information can also be accompanied by location information. When the location information is accompanied, the user terminal needs to open the location authorization. When the location information is accompanied, the required location is determined as the location information of the user terminal, which is more convenient to use. Of course, the required location can be determined by the user himself, with a higher degree of freedom. Next, the embodiment of the present invention determines the distance value of each drone relative to the required location and the safety factor of the current monitoring location, and determines the drone number going to the required location based on the distance value and the safety factor. It is easy to understand that drones with low distance values ​​and low safety factors are preferably dispatched. A dispatch base number is determined here, and the dispatch base number = k1*distance value+k2*safety factor. k1 and k2 are constant coefficients. The drone with the lowest dispatch base number will go to the required location. When the drone going to the required location arrives, it will track and shoot based on the monitoring target information. At this time, the monitoring target information will be used as the target feature. The monitoring target information can be vehicle features, human clothing features, etc.

[0068] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of identifying the image information based on the security feature and performing fixed-point tracking shooting when the image information contains the security feature specifically includes:

[0069] S201, extracting feature information from the image information based on a computer vision algorithm;

[0070] S202, matching the feature information with the security feature, and when the matching degree is higher than the matching threshold, determining that the security feature exists in the image information, and marking the security feature as a target feature;

[0071] S203, start the tracking mode of the drone to perform fixed-point tracking and shooting of the target feature.

[0072] In the embodiment of the present invention, after obtaining the image information, the image is first preprocessed to improve the accuracy and efficiency of subsequent recognition. The preprocessing steps include image denoising, image enhancement, image size adjustment, etc. Then, feature information is extracted from the image information based on a computer vision algorithm, and then the feature information is matched with the security feature. When the matching degree is higher than the matching threshold, it is determined that there is a security feature in the image information, and the security feature is marked as a target feature. A security feature library is established in advance, and the security feature library contains several security features. When matching, an efficient matching algorithm is used, such as template matching, feature vector comparison, etc., to quickly find the most similar features. When a target feature is marked, the tracking mode of the drone is activated to perform fixed-point tracking and shooting of the target feature.

[0073] like Figure 3 As shown, as a preferred embodiment of the present invention, the step of determining the distance value of each drone relative to the required location and the safety factor of the current monitoring location specifically includes:

[0074] S401, retrieve the coordinate information of each drone, and determine the distance value of each drone relative to the required location according to the coordinate information;

[0075] S402, retrieve the current shooting picture of each drone, determine whether the current shooting picture has security features, and when the security features are present, determine the safety factor of the current monitoring location to be A, where A is a constant value; otherwise, execute the next step;

[0076] S403, identifying the crowding degree of people in the current shooting picture, and determining the safety factor according to the crowding degree of people.

[0077] In an embodiment of the present invention, when monitoring demand information uploaded by a user terminal is received, the coordinate information of each drone is automatically retrieved, and the distance value of each drone relative to the required location is determined based on the coordinate information; then the current shooting picture of each drone is retrieved to determine whether there is a security feature in the current shooting picture. When there is a security feature, the safety factor of the current monitoring location is determined to be A, where A is a constant; when there is no security feature, the degree of crowding of people in the current shooting picture is automatically identified, and the safety factor is determined based on the degree of crowding of people. It is easy to understand that the safety factor is proportional to the degree of crowding, and the safety factor B=K*R, where K is a constant coefficient and R represents the degree of crowding.

[0078] like Figure 4 As shown, as a preferred embodiment of the present invention, the step of identifying the crowding degree of people in the current shooting picture specifically includes:

[0079] S4031, extracting crowd features in the current shot image through CNN, where the crowd features include density, distribution and movement direction of the crowd;

[0080] S4032, generating a crowd density map based on the extracted crowd features, wherein each pixel value in the crowd density map represents the crowd density at the location;

[0081] S4033, integrating the crowd density map to obtain the total number of people in the current shooting picture and determine the crowding degree of the people.

[0082] In the embodiment of the present invention, in order to determine the degree of crowding, it is first necessary to perform denoising, image stabilization, and illumination correction on the current shot image, and then extract the crowd features in the current shot image through CNN (convolutional neural network), and the crowd features include the density, distribution, and movement direction of the crowd; the CNN model needs to be trained in advance, specifically, by training with annotated data sets (including photos of crowded and non-crowded scenes), so that the model can learn the characteristic representation of crowd crowding. Then, based on the extracted crowd features, a crowd density map is generated, and each pixel value in the crowd density map represents the crowd density at that location. The crowd density map is integrated to obtain the total number of people in the current shot image, and the degree of crowding of the people is determined. The degree of crowding can be set to several levels, and the higher the level, the more crowded it is.

[0083] like Figure 5 As shown, as a preferred embodiment of the present invention, the step of tracking and shooting based on the monitoring target information specifically includes:

[0084] S501, determining a required detection area with the required location as the center and a fixed value R as the radius;

[0085] S502, collecting an image of the area to be detected, identifying whether there is monitoring target information in the image, and if so, performing tracking shooting; otherwise, executing the next step;

[0086] S503, sending monitoring target confirmation information to the user terminal, receiving feedback information, and when the feedback information is updated monitoring target information, performing tracking and shooting based on the latest monitoring target information; when the feedback information is confirmed, identifying again whether there is monitoring target information in the image screen, and returning if not.

[0087] In the embodiment of the present invention, when tracking and shooting are performed according to the monitoring target information, the required detection area will first be determined with the required location as the center and a fixed value R as the radius, and R needs to be set in advance. Then the drone will collect the image screen of the required detection area, identify whether there is monitoring target information in the image screen, and if it exists, track and shoot; if it does not exist, send the monitoring target confirmation information to the user terminal, so that the user reconfirms, and the user needs to input feedback information. When the feedback information is the updated monitoring target information, the tracking and shooting is based on the latest monitoring target information; when the feedback information is confirmed, it indicates that the previous monitoring target information is used, and it is identified again whether there is monitoring target information in the image screen. If it does not exist, return and execute the original patrol plan.

[0088] like Figure 6 As shown, an embodiment of the present invention further provides a security drone patrol monitoring system, the system comprising:

[0089] A patrol path information module 100 is used to receive patrol path information, where the patrol path information includes a number of drone numbers, each drone number corresponding to a patrol path;

[0090] The security feature recognition module 200 is used to receive the image information uploaded by the drone, recognize the image information based on the security feature, and when the image information contains the security feature, perform fixed-point tracking and shooting to generate warning information;

[0091] The monitoring demand information module 300 is used to receive monitoring demand information uploaded by the user terminal, wherein the monitoring demand information includes demand location and monitoring target information;

[0092] The drone dispatching module 400 is used to determine the distance value of each drone relative to the required location and the safety factor of the current monitoring location, and determine the number of the drone to go to the required location according to the distance value and the safety factor;

[0093] The demand tracking and shooting module 500 is used to perform tracking and shooting based on the monitoring target information when the drone heading to the demand location arrives.

[0094] As a preferred embodiment of the present invention, the security feature recognition module 200 includes:

[0095] A feature information extraction unit, used to extract feature information from image information based on a computer vision algorithm;

[0096] A feature information matching unit, used to match the feature information with the security feature, and when the matching degree is higher than a matching threshold, determine that the security feature exists in the image information, and mark the security feature as a target feature;

[0097] The target feature tracking unit is used to start the tracking mode of the drone and perform fixed-point tracking and shooting of the target features.

[0098] As a preferred embodiment of the present invention, the drone dispatching module 400 includes:

[0099] A distance value determination unit, used to retrieve the coordinate information of each drone, and determine the distance value of each drone relative to the required location according to the coordinate information;

[0100] The first safety factor unit is used to retrieve the current shooting picture of each drone, determine whether the current shooting picture has security features, and when the security features exist, determine the safety factor of the current monitoring location to be A, where A is a fixed value; otherwise, execute the next step;

[0101] The second safety factor unit is used to identify the crowding degree of people in the current shooting picture and determine the safety factor according to the crowding degree of people.

[0102] As a preferred embodiment of the present invention, the second safety factor unit includes:

[0103] The crowd feature extraction subunit is used to extract crowd features in the current shot picture through CNN, where the crowd features include crowd density, distribution and movement direction;

[0104] A crowd density map subunit is used to generate a crowd density map based on the extracted crowd features, where each pixel value in the crowd density map represents the crowd density at that location;

[0105] The crowding degree subunit is used to integrate the crowd density map to obtain the total number of people in the current shooting picture and determine the crowding degree of the people.

[0106] As a preferred embodiment of the present invention, the demand tracking and shooting module 500 includes:

[0107] The demand detection area unit is used to determine the demand detection area with the demand location as the center and a fixed value R as the radius;

[0108] The monitoring target determination unit is used to collect the image of the required detection area, identify whether there is monitoring target information in the image, and if so, perform tracking and shooting; otherwise, execute the next step;

[0109] The monitoring target confirmation unit is used to send monitoring target confirmation information to the user terminal and receive feedback information. When the feedback information is updated monitoring target information, tracking and shooting are performed based on the latest monitoring target information. When the feedback information is confirmed, it is re-identified whether there is monitoring target information in the image screen. If not, return.

[0110] The above only describes in detail the preferred embodiments of the present invention, which is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0111] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0112] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0113] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A security drone patrol monitoring method, characterized in that: The method comprises the following steps: Receiving patrol path information, where the patrol path information includes a number of drone numbers, each drone number corresponding to a patrol path; Receive image information uploaded by drones, identify the image information based on security features, and when security features exist in the image information, perform fixed-point tracking and shooting to generate early warning information; Receiving monitoring demand information uploaded by a user terminal, wherein the monitoring demand information includes a demand location and monitoring target information; Determine the distance value of each drone relative to the required location and the safety factor of the current monitoring location, and determine the drone number to go to the required location based on the distance value and safety factor; When the drone arrives at the required location, it will track and shoot based on the monitoring target information.

2. The security drone patrol monitoring method according to claim 1 is characterized in that: The step of identifying the image information based on the security feature and performing fixed-point tracking shooting when the image information contains the security feature specifically includes: Extract feature information from image information based on computer vision algorithms; Matching the feature information with the security feature, and when the matching degree is higher than the matching threshold, determining that the security feature exists in the image information, and marking the security feature as a target feature; Start the tracking mode of the drone and perform fixed-point tracking and shooting of target features.

3. The security drone patrol monitoring method according to claim 1 is characterized in that: The step of determining the distance value of each drone relative to the required location and the safety factor of the current monitoring location specifically includes: Retrieve the coordinate information of each drone, and determine the distance value of each drone relative to the required location based on the coordinate information; Retrieve the current shooting picture of each drone, determine whether the current shooting picture has security features, and when there are security features, determine the safety factor of the current monitoring location to be A, where A is a fixed value; otherwise, execute the next step; Identify the crowding level of people in the current shooting picture and determine the safety factor based on the crowding level of people.

4. The security drone patrol monitoring method according to claim 3 is characterized in that: The step of identifying the crowding degree of people in the current shooting picture specifically includes: Extracting crowd features in the current shot image through CNN, the crowd features include crowd density, distribution and movement direction; Based on the extracted crowd features, a crowd density map is generated, and each pixel value in the crowd density map represents the crowd density at that location; Integrate the crowd density map to obtain the total number of people in the current shooting picture and determine the degree of crowding.

5. The security drone patrol monitoring method according to claim 3 is characterized in that: The safety factor is proportional to the degree of congestion, and the safety factor B=K*R, wherein K is a constant coefficient and R represents the degree of congestion.

6. The security drone patrol monitoring method according to claim 1 is characterized in that: The step of performing tracking shooting based on the monitoring target information specifically includes: Determine the required detection area with the required location as the center and a fixed value R as the radius; Collect the image of the required detection area, identify whether there is monitoring target information in the image, and if so, perform tracking shooting; otherwise, execute the next step; The system sends monitoring target confirmation information to the user terminal and receives feedback information. When the feedback information is updated monitoring target information, the system performs tracking and shooting based on the latest monitoring target information. When the feedback information is confirmed, the system identifies whether the monitoring target information exists in the image again. If not, the system returns home.

7. The security drone patrol monitoring method according to claim 1, characterized in that: The monitoring demand information may also include location information. When the location information is included, the user terminal needs to enable location authorization. When the location information is included, the required location is determined to be the location information of the user terminal.

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